+ 
+
+
+바운딩 박스와 연결된 레이블을 시각화하려면 데이터 세트의 메타 데이터, 특히 `category` 필드에서 레이블을 가져와야 합니다.
+또한 레이블 ID를 레이블 클래스에 매핑하는 `id2label`과 반대로 매핑하는 `label2id` 딕셔너리를 만들어야 합니다.
+모델을 설정할 때 이러한 매핑을 사용할 수 있습니다. 이러한 매핑은 허깅페이스 허브에서 모델을 공유했을 때 다른 사람들이 재사용할 수 있습니다.
+
+데이터를 더 잘 이해하기 위한 최종 단계로, 잠재적인 문제를 찾아보세요.
+객체 감지를 위한 데이터 세트에서 자주 발생하는 문제 중 하나는 바운딩 박스가 이미지의 가장자리를 넘어가는 것입니다.
+이러한 바운딩 박스를 "넘어가는 것(run away)"은 훈련 중에 오류를 발생시킬 수 있기에 이 단계에서 처리해야 합니다.
+이 데이터 세트에도 같은 문제가 있는 몇 가지 예가 있습니다. 이 가이드에서는 간단하게하기 위해 데이터에서 이러한 이미지를 제거합니다.
+
+```py
+>>> remove_idx = [590, 821, 822, 875, 876, 878, 879]
+>>> keep = [i for i in range(len(cppe5["train"])) if i not in remove_idx]
+>>> cppe5["train"] = cppe5["train"].select(keep)
+```
+
+## 데이터 전처리하기 [[preprocess-the-data]]
+
+모델을 미세 조정 하려면, 미리 학습된 모델에서 사용한 전처리 방식과 정확하게 일치하도록 사용할 데이터를 전처리해야 합니다.
+[`AutoImageProcessor`]는 이미지 데이터를 처리하여 DETR 모델이 학습에 사용할 수 있는 `pixel_values`, `pixel_mask`, 그리고 `labels`를 생성하는 작업을 담당합니다.
+이 이미지 프로세서에는 걱정하지 않아도 되는 몇 가지 속성이 있습니다:
+
+- `image_mean = [0.485, 0.456, 0.406 ]`
+- `image_std = [0.229, 0.224, 0.225]`
+
+
+이 값들은 모델 사전 훈련 중 이미지를 정규화하는 데 사용되는 평균과 표준 편차입니다.
+이 값들은 추론 또는 사전 훈련된 이미지 모델을 세밀하게 조정할 때 복제해야 하는 중요한 값입니다.
+
+사전 훈련된 모델과 동일한 체크포인트에서 이미지 프로세서를 인스턴스화합니다.
+
+```py
+>>> from transformers import AutoImageProcessor
+
+>>> checkpoint = "facebook/detr-resnet-50"
+>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
+```
+
+`image_processor`에 이미지를 전달하기 전에, 데이터 세트에 두 가지 전처리를 적용해야 합니다:
+
+- 이미지 증강
+- DETR 모델의 요구에 맞게 어노테이션을 다시 포맷팅
+
+첫째로, 모델이 학습 데이터에 과적합 되지 않도록 데이터 증강 라이브러리 중 아무거나 사용하여 변환을 적용할 수 있습니다. 여기에서는 [Albumentations](https://albumentations.ai/docs/) 라이브러리를 사용합니다...
+이 라이브러리는 변환을 이미지에 적용하고 바운딩 박스를 적절하게 업데이트하도록 보장합니다.
+🤗 Datasets 라이브러리 문서에는 [객체 탐지를 위해 이미지를 보강하는 방법에 대한 자세한 가이드](https://huggingface.co/docs/datasets/object_detection)가 있으며,
+이 예제와 정확히 동일한 데이터 세트를 사용합니다. 여기서는 각 이미지를 (480, 480) 크기로 조정하고, 좌우로 뒤집고, 밝기를 높이는 동일한 접근법을 적용합니다:
+
+
+```py
+>>> import albumentations
+>>> import numpy as np
+>>> import torch
+
+>>> transform = albumentations.Compose(
+... [
+... albumentations.Resize(480, 480),
+... albumentations.HorizontalFlip(p=1.0),
+... albumentations.RandomBrightnessContrast(p=1.0),
+... ],
+... bbox_params=albumentations.BboxParams(format="coco", label_fields=["category"]),
+... )
+```
+
+이미지 프로세서는 어노테이션이 다음과 같은 형식일 것으로 예상합니다: `{'image_id': int, 'annotations': list[Dict]}`, 여기서 각 딕셔너리는 COCO 객체 어노테이션입니다. 단일 예제에 대해 어노테이션의 형식을 다시 지정하는 함수를 추가해 보겠습니다:
+
+```py
+>>> def formatted_anns(image_id, category, area, bbox):
+... annotations = []
+... for i in range(0, len(category)):
+... new_ann = {
+... "image_id": image_id,
+... "category_id": category[i],
+... "isCrowd": 0,
+... "area": area[i],
+... "bbox": list(bbox[i]),
+... }
+... annotations.append(new_ann)
+
+... return annotations
+```
+
+이제 이미지와 어노테이션 전처리 변환을 결합하여 예제 배치에 사용할 수 있습니다:
+
+```py
+>>> # transforming a batch
+>>> def transform_aug_ann(examples):
+... image_ids = examples["image_id"]
+... images, bboxes, area, categories = [], [], [], []
+... for image, objects in zip(examples["image"], examples["objects"]):
+... image = np.array(image.convert("RGB"))[:, :, ::-1]
+... out = transform(image=image, bboxes=objects["bbox"], category=objects["category"])
+
+... area.append(objects["area"])
+... images.append(out["image"])
+... bboxes.append(out["bboxes"])
+... categories.append(out["category"])
+
+... targets = [
+... {"image_id": id_, "annotations": formatted_anns(id_, cat_, ar_, box_)}
+... for id_, cat_, ar_, box_ in zip(image_ids, categories, area, bboxes)
+... ]
+
+... return image_processor(images=images, annotations=targets, return_tensors="pt")
+```
+
+이전 단계에서 만든 전처리 함수를 🤗 Datasets의 [`~datasets.Dataset.with_transform`] 메소드를 사용하여 데이터 세트 전체에 적용합니다.
+이 메소드는 데이터 세트의 요소를 가져올 때마다 전처리 함수를 적용합니다.
+
+이 시점에서는 전처리 후 데이터 세트에서 예시 하나를 가져와서 변환 후 모양이 어떻게 되는지 확인해 볼 수 있습니다.
+이때, `pixel_values` 텐서, `pixel_mask` 텐서, 그리고 `labels`로 구성된 텐서가 있어야 합니다.
+
+```py
+>>> cppe5["train"] = cppe5["train"].with_transform(transform_aug_ann)
+>>> cppe5["train"][15]
+{'pixel_values': tensor([[[ 0.9132, 0.9132, 0.9132, ..., -1.9809, -1.9809, -1.9809],
+ [ 0.9132, 0.9132, 0.9132, ..., -1.9809, -1.9809, -1.9809],
+ [ 0.9132, 0.9132, 0.9132, ..., -1.9638, -1.9638, -1.9638],
+ ...,
+ [-1.5699, -1.5699, -1.5699, ..., -1.9980, -1.9980, -1.9980],
+ [-1.5528, -1.5528, -1.5528, ..., -1.9980, -1.9809, -1.9809],
+ [-1.5528, -1.5528, -1.5528, ..., -1.9980, -1.9809, -1.9809]],
+
+ [[ 1.3081, 1.3081, 1.3081, ..., -1.8431, -1.8431, -1.8431],
+ [ 1.3081, 1.3081, 1.3081, ..., -1.8431, -1.8431, -1.8431],
+ [ 1.3081, 1.3081, 1.3081, ..., -1.8256, -1.8256, -1.8256],
+ ...,
+ [-1.3179, -1.3179, -1.3179, ..., -1.8606, -1.8606, -1.8606],
+ [-1.3004, -1.3004, -1.3004, ..., -1.8606, -1.8431, -1.8431],
+ [-1.3004, -1.3004, -1.3004, ..., -1.8606, -1.8431, -1.8431]],
+
+ [[ 1.4200, 1.4200, 1.4200, ..., -1.6476, -1.6476, -1.6476],
+ [ 1.4200, 1.4200, 1.4200, ..., -1.6476, -1.6476, -1.6476],
+ [ 1.4200, 1.4200, 1.4200, ..., -1.6302, -1.6302, -1.6302],
+ ...,
+ [-1.0201, -1.0201, -1.0201, ..., -1.5604, -1.5604, -1.5604],
+ [-1.0027, -1.0027, -1.0027, ..., -1.5604, -1.5430, -1.5430],
+ [-1.0027, -1.0027, -1.0027, ..., -1.5604, -1.5430, -1.5430]]]),
+ 'pixel_mask': tensor([[1, 1, 1, ..., 1, 1, 1],
+ [1, 1, 1, ..., 1, 1, 1],
+ [1, 1, 1, ..., 1, 1, 1],
+ ...,
+ [1, 1, 1, ..., 1, 1, 1],
+ [1, 1, 1, ..., 1, 1, 1],
+ [1, 1, 1, ..., 1, 1, 1]]),
+ 'labels': {'size': tensor([800, 800]), 'image_id': tensor([756]), 'class_labels': tensor([4]), 'boxes': tensor([[0.7340, 0.6986, 0.3414, 0.5944]]), 'area': tensor([519544.4375]), 'iscrowd': tensor([0]), 'orig_size': tensor([480, 480])}}
+```
+
+각각의 이미지를 성공적으로 증강하고 이미지의 어노테이션을 준비했습니다.
+그러나 전처리는 아직 끝나지 않았습니다. 마지막 단계로, 이미지를 배치로 만들 사용자 정의 `collate_fn`을 생성합니다.
+해당 배치에서 가장 큰 이미지에 이미지(현재 `pixel_values` 인)를 패드하고, 실제 픽셀(1)과 패딩(0)을 나타내기 위해 그에 해당하는 새로운 `pixel_mask`를 생성해야 합니다.
+
+```py
+>>> def collate_fn(batch):
+... pixel_values = [item["pixel_values"] for item in batch]
+... encoding = image_processor.pad(pixel_values, return_tensors="pt")
+... labels = [item["labels"] for item in batch]
+... batch = {}
+... batch["pixel_values"] = encoding["pixel_values"]
+... batch["pixel_mask"] = encoding["pixel_mask"]
+... batch["labels"] = labels
+... return batch
+```
+
+## DETR 모델 학습시키기 [[training-the-DETR-model]]
+
+이전 섹션에서 대부분의 작업을 수행하여 이제 모델을 학습할 준비가 되었습니다!
+이 데이터 세트의 이미지는 리사이즈 후에도 여전히 용량이 크기 때문에, 이 모델을 미세 조정 하려면 적어도 하나의 GPU가 필요합니다.
+
+학습은 다음의 단계를 수행합니다:
+
+1. [`AutoModelForObjectDetection`]을 사용하여 전처리와 동일한 체크포인트를 사용하여 모델을 가져옵니다.
+2. [`TrainingArguments`]에서 학습 하이퍼파라미터를 정의합니다.
+3. 모델, 데이터 세트, 이미지 프로세서 및 데이터 콜레이터와 함께 [`Trainer`]에 훈련 인수를 전달합니다.
+4. [`~Trainer.train`]를 호출하여 모델을 미세 조정 합니다.
+
+전처리에 사용한 체크포인트와 동일한 체크포인트에서 모델을 가져올 때, 데이터 세트의 메타데이터에서 만든 `label2id`와 `id2label` 매핑을 전달해야 합니다.
+또한, `ignore_mismatched_sizes=True`를 지정하여 기존 분류 헤드(모델에서 분류에 사용되는 마지막 레이어)를 새 분류 헤드로 대체합니다.
+
+```py
+>>> from transformers import AutoModelForObjectDetection
+
+>>> model = AutoModelForObjectDetection.from_pretrained(
+... checkpoint,
+... id2label=id2label,
+... label2id=label2id,
+... ignore_mismatched_sizes=True,
+... )
+```
+
+[`TrainingArguments`]에서 `output_dir`을 사용하여 모델을 저장할 위치를 지정한 다음, 필요에 따라 하이퍼파라미터를 구성하세요.
+사용하지 않는 열을 제거하지 않도록 주의해야 합니다. 만약 `remove_unused_columns`가 `True`일 경우 이미지 열이 삭제됩니다.
+이미지 열이 없는 경우 `pixel_values`를 생성할 수 없기 때문에 `remove_unused_columns`를 `False`로 설정해야 합니다.
+모델을 Hub에 업로드하여 공유하려면 `push_to_hub`를 `True`로 설정하십시오(허깅페이스에 로그인하여 모델을 업로드해야 합니다).
+
+
+```py
+>>> from transformers import TrainingArguments
+
+>>> training_args = TrainingArguments(
+... output_dir="detr-resnet-50_finetuned_cppe5",
+... per_device_train_batch_size=8,
+... num_train_epochs=10,
+... fp16=True,
+... save_steps=200,
+... logging_steps=50,
+... learning_rate=1e-5,
+... weight_decay=1e-4,
+... save_total_limit=2,
+... remove_unused_columns=False,
+... push_to_hub=True,
+... )
+```
+
+마지막으로 `model`, `training_args`, `collate_fn`, `image_processor`와 데이터 세트(`cppe5`)를 모두 가져온 후, [`~transformers.Trainer.train`]를 호출합니다.
+
+```py
+>>> from transformers import Trainer
+
+>>> trainer = Trainer(
+... model=model,
+... args=training_args,
+... data_collator=collate_fn,
+... train_dataset=cppe5["train"],
+... processing_class=image_processor,
+... )
+
+>>> trainer.train()
+```
+
+`training_args`에서 `push_to_hub`를 `True`로 설정한 경우, 학습 체크포인트는 허깅페이스 허브에 업로드됩니다.
+학습 완료 후, [`~transformers.Trainer.push_to_hub`] 메소드를 호출하여 최종 모델을 허깅페이스 허브에 업로드합니다.
+
+```py
+>>> trainer.push_to_hub()
+```
+
+## 평가하기 [[evaluate]]
+
+객체 탐지 모델은 일반적으로 일련의 COCO-스타일 지표로 평가됩니다.
+기존에 구현된 평가 지표 중 하나를 사용할 수도 있지만, 여기에서는 허깅페이스 허브에 푸시한 최종 모델을 평가하는 데 `torchvision`에서 제공하는 평가 지표를 사용합니다.
+
+`torchvision` 평가자(evaluator)를 사용하려면 실측값인 COCO 데이터 세트를 준비해야 합니다.
+COCO 데이터 세트를 빌드하는 API는 데이터를 특정 형식으로 저장해야 하므로, 먼저 이미지와 어노테이션을 디스크에 저장해야 합니다.
+학습을 위해 데이터를 준비할 때와 마찬가지로, cppe5["test"]에서의 어노테이션은 포맷을 맞춰야 합니다. 그러나 이미지는 그대로 유지해야 합니다.
+
+평가 단계는 약간의 작업이 필요하지만, 크게 세 가지 주요 단계로 나눌 수 있습니다.
+먼저, `cppe5["test"]` 세트를 준비합니다: 어노테이션을 포맷에 맞게 만들고 데이터를 디스크에 저장합니다.
+
+```py
+>>> import json
+
+
+>>> # format annotations the same as for training, no need for data augmentation
+>>> def val_formatted_anns(image_id, objects):
+... annotations = []
+... for i in range(0, len(objects["id"])):
+... new_ann = {
+... "id": objects["id"][i],
+... "category_id": objects["category"][i],
+... "iscrowd": 0,
+... "image_id": image_id,
+... "area": objects["area"][i],
+... "bbox": objects["bbox"][i],
+... }
+... annotations.append(new_ann)
+
+... return annotations
+
+
+>>> # Save images and annotations into the files torchvision.datasets.CocoDetection expects
+>>> def save_cppe5_annotation_file_images(cppe5):
+... output_json = {}
+... path_output_cppe5 = f"{os.getcwd()}/cppe5/"
+
+... if not os.path.exists(path_output_cppe5):
+... os.makedirs(path_output_cppe5)
+
+... path_anno = os.path.join(path_output_cppe5, "cppe5_ann.json")
+... categories_json = [{"supercategory": "none", "id": id, "name": id2label[id]} for id in id2label]
+... output_json["images"] = []
+... output_json["annotations"] = []
+... for example in cppe5:
+... ann = val_formatted_anns(example["image_id"], example["objects"])
+... output_json["images"].append(
+... {
+... "id": example["image_id"],
+... "width": example["image"].width,
+... "height": example["image"].height,
+... "file_name": f"{example['image_id']}.png",
+... }
+... )
+... output_json["annotations"].extend(ann)
+... output_json["categories"] = categories_json
+
+... with open(path_anno, "w") as file:
+... json.dump(output_json, file, ensure_ascii=False, indent=4)
+
+... for im, img_id in zip(cppe5["image"], cppe5["image_id"]):
+... path_img = os.path.join(path_output_cppe5, f"{img_id}.png")
+... im.save(path_img)
+
+... return path_output_cppe5, path_anno
+```
+
+다음으로, `cocoevaluator`와 함께 사용할 수 있는 `CocoDetection` 클래스의 인스턴스를 준비합니다.
+
+```py
+>>> import torchvision
+
+
+>>> class CocoDetection(torchvision.datasets.CocoDetection):
+... def __init__(self, img_folder, image_processor, ann_file):
+... super().__init__(img_folder, ann_file)
+... self.image_processor = image_processor
+
+... def __getitem__(self, idx):
+... # read in PIL image and target in COCO format
+... img, target = super(CocoDetection, self).__getitem__(idx)
+
+... # preprocess image and target: converting target to DETR format,
+... # resizing + normalization of both image and target)
+... image_id = self.ids[idx]
+... target = {"image_id": image_id, "annotations": target}
+... encoding = self.image_processor(images=img, annotations=target, return_tensors="pt")
+... pixel_values = encoding["pixel_values"].squeeze() # remove batch dimension
+... target = encoding["labels"][0] # remove batch dimension
+
+... return {"pixel_values": pixel_values, "labels": target}
+
+
+>>> im_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
+
+>>> path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"])
+>>> test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno)
+```
+
+마지막으로, 평가 지표를 가져와서 평가를 실행합니다.
+
+```py
+>>> import evaluate
+>>> from tqdm import tqdm
+
+>>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
+>>> module = evaluate.load("ybelkada/cocoevaluate", coco=test_ds_coco_format.coco)
+>>> val_dataloader = torch.utils.data.DataLoader(
+... test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn
+... )
+
+>>> with torch.no_grad():
+... for idx, batch in enumerate(tqdm(val_dataloader)):
+... pixel_values = batch["pixel_values"]
+... pixel_mask = batch["pixel_mask"]
+
+... labels = [
+... {k: v for k, v in t.items()} for t in batch["labels"]
+... ] # these are in DETR format, resized + normalized
+
+... # forward pass
+... outputs = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
+
+... orig_target_sizes = torch.stack([target["orig_size"] for target in labels], dim=0)
+... results = im_processor.post_process(outputs, orig_target_sizes) # convert outputs of model to Pascal VOC format (xmin, ymin, xmax, ymax)
+
+... module.add(prediction=results, reference=labels)
+... del batch
+
+>>> results = module.compute()
+>>> print(results)
+Accumulating evaluation results...
+DONE (t=0.08s).
+IoU metric: bbox
+ Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.352
+ Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.681
+ Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.292
+ Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168
+ Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.208
+ Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.429
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.274
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.484
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.501
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.191
+ Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.323
+ Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.590
+```
+
+이러한 결과는 [`~transformers.TrainingArguments`]의 하이퍼파라미터를 조정하여 더욱 개선될 수 있습니다. 한번 시도해 보세요!
+
+## 추론하기 [[inference]]
+
+DETR 모델을 미세 조정 및 평가하고, 허깅페이스 허브에 업로드 했으므로 추론에 사용할 수 있습니다.
+
+미세 조정된 모델을 추론에 사용하는 가장 간단한 방법은 [`pipeline`]에서 모델을 사용하는 것입니다.
+모델과 함께 객체 탐지를 위한 파이프라인을 인스턴스화하고, 이미지를 전달하세요:
+
+```py
+>>> from transformers import pipeline
+>>> import requests
+
+>>> url = "https://i.imgur.com/2lnWoly.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+
+>>> obj_detector = pipeline("object-detection", model="devonho/detr-resnet-50_finetuned_cppe5")
+>>> obj_detector(image)
+```
+
+만약 원한다면 수동으로 `pipeline`의 결과를 재현할 수 있습니다:
+
+```py
+>>> image_processor = AutoImageProcessor.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
+>>> model = AutoModelForObjectDetection.from_pretrained("devonho/detr-resnet-50_finetuned_cppe5")
+
+>>> with torch.no_grad():
+... inputs = image_processor(images=image, return_tensors="pt")
+... outputs = model(**inputs)
+... target_sizes = torch.tensor([image.size[::-1]])
+... results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
+
+>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+... box = [round(i, 2) for i in box.tolist()]
+... print(
+... f"Detected {model.config.id2label[label.item()]} with confidence "
+... f"{round(score.item(), 3)} at location {box}"
+... )
+Detected Coverall with confidence 0.566 at location [1215.32, 147.38, 4401.81, 3227.08]
+Detected Mask with confidence 0.584 at location [2449.06, 823.19, 3256.43, 1413.9]
+```
+
+결과를 시각화하겠습니다:
+```py
+>>> draw = ImageDraw.Draw(image)
+
+>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+... box = [round(i, 2) for i in box.tolist()]
+... x, y, x2, y2 = tuple(box)
+... draw.rectangle((x, y, x2, y2), outline="red", width=1)
+... draw.text((x, y), model.config.id2label[label.item()], fill="white")
+
+>>> image
+```
+
+
+
+ 
+
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/prompting.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/prompting.md
new file mode 100644
index 0000000000000000000000000000000000000000..8f154dbe74c913ce34ae9a614a6705eb7b4ba13a
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/prompting.md
@@ -0,0 +1,384 @@
+
+
+
+# 대규모 언어 모델(LLM) 프롬프팅 가이드 [[llm-prompting-guide]]
+
+[[open-in-colab]]
+
+Falcon, LLaMA 등의 대규모 언어 모델은 사전 훈련된 트랜스포머 모델로, 초기에는 주어진 입력 텍스트에 대해 다음 토큰을 예측하도록 훈련됩니다. 이들은 보통 수십억 개의 매개변수를 가지고 있으며, 장기간에 걸쳐 수조 개의 토큰으로 훈련됩니다. 그 결과, 이 모델들은 매우 강력하고 다재다능해져서, 자연어 프롬프트로 모델에 지시하여 다양한 자연어 처리 작업을 즉시 수행할 수 있습니다.
+
+최적의 출력을 보장하기 위해 이러한 프롬프트를 설계하는 것을 흔히 "프롬프트 엔지니어링"이라고 합니다. 프롬프트 엔지니어링은 상당한 실험이 필요한 반복적인 과정입니다. 자연어는 프로그래밍 언어보다 훨씬 유연하고 표현력이 풍부하지만, 동시에 모호성을 초래할 수 있습니다. 또한, 자연어 프롬프트는 변화에 매우 민감합니다. 프롬프트의 사소한 수정만으로도 완전히 다른 출력이 나올 수 있습니다.
+
+모든 경우에 적용할 수 있는 정확한 프롬프트 생성 공식은 없지만, 연구자들은 더 일관되게 최적의 결과를 얻는 데 도움이 되는 여러 가지 모범 사례를 개발했습니다.
+
+이 가이드에서는 더 나은 대규모 언어 모델 프롬프트를 작성하고 다양한 자연어 처리 작업을 해결하는 데 도움이 되는 프롬프트 엔지니어링 모범 사례를 다룹니다:
+
+- [프롬프팅의 기초](#basics-of-prompting)
+- [대규모 언어 모델 프롬프팅의 모범 사례](#best-practices-of-llm-prompting)
+- [고급 프롬프팅 기법: 퓨샷(Few-shot) 프롬프팅과 생각의 사슬(Chain-of-thought, CoT) 기법](#advanced-prompting-techniques)
+- [프롬프팅 대신 미세 조정을 해야 하는 경우](#prompting-vs-fine-tuning)
+
+
+
+ 
+
+
+
+
+ 
+
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/sequence_classification.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/sequence_classification.md
new file mode 100644
index 0000000000000000000000000000000000000000..11dae1a965a4f2634cf304bb8c805715e2791f9b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/sequence_classification.md
@@ -0,0 +1,389 @@
+
+
+# 텍스트 분류[[text-classification]]
+
+[[open-in-colab]]
+
+
+
+ 
+
+
+## 모델 훈련하기[[train-the-model]]
+
+🤗 Transformers의 [`Trainer`](https://huggingface.co/docs/transformers/main_classes/trainer)를 사용하여 모델을 훈련시켜보세요. `Trainer`를 인스턴스화하려면 훈련 설정과 평가 지표를 정의해야 합니다. 가장 중요한 것은 [`TrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments)입니다. 이 클래스는 훈련을 구성하는 모든 속성을 포함하며, 훈련 중 체크포인트를 저장할 출력 폴더 이름을 필요로 합니다. 또한 🤗 Hub의 모델 저장소의 모든 정보를 동기화하는 데 도움이 됩니다.
+
+대부분의 훈련 인수는 따로 설명할 필요는 없습니다. 하지만 여기에서 중요한 인수는 `remove_unused_columns=False` 입니다. 이 인자는 모델의 호출 함수에서 사용되지 않는 모든 속성 열(columns)을 삭제합니다. 기본값은 일반적으로 True입니다. 이는 사용되지 않는 기능 열을 삭제하는 것이 이상적이며, 입력을 모델의 호출 함수로 풀기(unpack)가 쉬워지기 때문입니다. 하지만 이 경우에는 `pixel_values`(모델의 입력으로 필수적인 키)를 생성하기 위해 사용되지 않는 기능('video'가 특히 그렇습니다)이 필요합니다. 따라서 remove_unused_columns을 False로 설정해야 합니다.
+
+```py
+>>> from transformers import TrainingArguments, Trainer
+
+>>> model_name = model_ckpt.split("/")[-1]
+>>> new_model_name = f"{model_name}-finetuned-ucf101-subset"
+>>> num_epochs = 4
+
+>>> args = TrainingArguments(
+... new_model_name,
+... remove_unused_columns=False,
+... eval_strategy="epoch",
+... save_strategy="epoch",
+... learning_rate=5e-5,
+... per_device_train_batch_size=batch_size,
+... per_device_eval_batch_size=batch_size,
+... warmup_ratio=0.1,
+... logging_steps=10,
+... load_best_model_at_end=True,
+... metric_for_best_model="accuracy",
+... push_to_hub=True,
+... max_steps=(train_dataset.num_videos // batch_size) * num_epochs,
+... )
+```
+
+`pytorchvideo.data.Ucf101()` 함수로 반환되는 데이터 세트는 `__len__` 메소드가 이식되어 있지 않습니다. 따라서, `TrainingArguments`를 인스턴스화할 때 `max_steps`를 정의해야 합니다.
+
+다음으로, 평가지표를 불러오고, 예측값에서 평가지표를 계산할 함수를 정의합니다. 필요한 전처리 작업은 예측된 로짓(logits)에 argmax 값을 취하는 것뿐입니다:
+
+```py
+import evaluate
+
+metric = evaluate.load("accuracy")
+
+
+def compute_metrics(eval_pred):
+ predictions = np.argmax(eval_pred.predictions, axis=1)
+ return metric.compute(predictions=predictions, references=eval_pred.label_ids)
+```
+
+**평가에 대한 참고사항**:
+
+[VideoMAE 논문](https://huggingface.co/papers/2203.12602)에서 저자는 다음과 같은 평가 전략을 사용합니다. 테스트 영상에서 여러 클립을 선택하고 그 클립에 다양한 크롭을 적용하여 집계 점수를 보고합니다. 그러나 이번 튜토리얼에서는 간단함과 간결함을 위해 해당 전략을 고려하지 않습니다.
+
+또한, 예제를 묶어서 배치를 형성하는 `collate_fn`을 정의해야합니다. 각 배치는 `pixel_values`와 `labels`라는 2개의 키로 구성됩니다.
+
+```py
+>>> def collate_fn(examples):
+... # permute to (num_frames, num_channels, height, width)
+... pixel_values = torch.stack(
+... [example["video"].permute(1, 0, 2, 3) for example in examples]
+... )
+... labels = torch.tensor([example["label"] for example in examples])
+... return {"pixel_values": pixel_values, "labels": labels}
+```
+
+그런 다음 이 모든 것을 데이터 세트와 함께 `Trainer`에 전달하기만 하면 됩니다:
+
+```py
+>>> trainer = Trainer(
+... model,
+... args,
+... train_dataset=train_dataset,
+... eval_dataset=val_dataset,
+... processing_class=image_processor,
+... compute_metrics=compute_metrics,
+... data_collator=collate_fn,
+... )
+```
+
+데이터를 이미 처리했는데도 불구하고 `image_processor`를 토크나이저 인수로 넣은 이유는 JSON으로 저장되는 이미지 프로세서 구성 파일이 Hub의 저장소에 업로드되도록 하기 위함입니다.
+
+`train` 메소드를 호출하여 모델을 미세 조정하세요:
+
+```py
+>>> train_results = trainer.train()
+```
+
+학습이 완료되면, 모델을 [`~transformers.Trainer.push_to_hub`] 메소드를 사용하여 허브에 공유하여 누구나 모델을 사용할 수 있도록 합니다:
+```py
+>>> trainer.push_to_hub()
+```
+
+## 추론하기[[inference]]
+
+좋습니다. 이제 미세 조정된 모델을 추론하는 데 사용할 수 있습니다.
+
+추론에 사용할 영상을 불러오세요:
+```py
+>>> sample_test_video = next(iter(test_dataset))
+```
+
+
+
+ 
+
+
+미세 조정된 모델을 추론에 사용하는 가장 간단한 방법은 [`pipeline`](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.VideoClassificationPipeline)에서 모델을 사용하는 것입니다. 모델로 영상 분류를 하기 위해 `pipeline`을 인스턴스화하고 영상을 전달하세요:
+
+```py
+>>> from transformers import pipeline
+
+>>> video_cls = pipeline(model="my_awesome_video_cls_model")
+>>> video_cls("https://huggingface.co/datasets/sayakpaul/ucf101-subset/resolve/main/v_BasketballDunk_g14_c06.avi")
+[{'score': 0.9272987842559814, 'label': 'BasketballDunk'},
+ {'score': 0.017777055501937866, 'label': 'BabyCrawling'},
+ {'score': 0.01663011871278286, 'label': 'BalanceBeam'},
+ {'score': 0.009560945443809032, 'label': 'BandMarching'},
+ {'score': 0.0068979403004050255, 'label': 'BaseballPitch'}]
+```
+
+만약 원한다면 수동으로 `pipeline`의 결과를 재현할 수 있습니다:
+
+
+```py
+>>> def run_inference(model, video):
+... # (num_frames, num_channels, height, width)
+... perumuted_sample_test_video = video.permute(1, 0, 2, 3)
+... inputs = {
+... "pixel_values": perumuted_sample_test_video.unsqueeze(0),
+... "labels": torch.tensor(
+... [sample_test_video["label"]]
+... ), # this can be skipped if you don't have labels available.
+... }
+
+... device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+... inputs = {k: v.to(device) for k, v in inputs.items()}
+... model = model.to(device)
+
+... # forward pass
+... with torch.no_grad():
+... outputs = model(**inputs)
+... logits = outputs.logits
+
+... return logits
+```
+
+모델에 입력값을 넣고 `logits`을 반환받으세요:
+
+```py
+>>> logits = run_inference(trained_model, sample_test_video["video"])
+```
+
+`logits`을 디코딩하면, 우리는 다음 결과를 얻을 수 있습니다:
+
+```py
+>>> predicted_class_idx = logits.argmax(-1).item()
+>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
+# Predicted class: BasketballDunk
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/visual_question_answering.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/visual_question_answering.md
new file mode 100644
index 0000000000000000000000000000000000000000..9bc87c071e62b124ed6c941d83242c845fb9aeab
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/visual_question_answering.md
@@ -0,0 +1,375 @@
+
+
+# 시각적 질의응답 (Visual Question Answering)
+
+[[open-in-colab]]
+
+시각적 질의응답(VQA)은 이미지를 기반으로 개방형 질문에 대응하는 작업입니다. 이 작업을 지원하는 모델의 입력은 대부분 이미지와 질문의 조합이며, 출력은 자연어로 된 답변입니다.
+
+VQA의 주요 사용 사례는 다음과 같습니다:
+* 시각 장애인을 위한 접근성 애플리케이션을 구축할 수 있습니다.
+* 교육: 강의나 교과서에 나온 시각 자료에 대한 질문에 답할 수 있습니다. 또한 체험형 전시와 유적 등에서도 VQA를 활용할 수 있습니다.
+* 고객 서비스 및 전자상거래: VQA는 사용자가 제품에 대해 질문할 수 있게 함으로써 사용자 경험을 향상시킬 수 있습니다.
+* 이미지 검색: VQA 모델을 사용하여 원하는 특성을 가진 이미지를 검색할 수 있습니다. 예를 들어 사용자는 "강아지가 있어?"라고 물어봐서 주어진 이미지 묶음에서 강아지가 있는 모든 이미지를 받아볼 수 있습니다.
+
+이 가이드에서 학습할 내용은 다음과 같습니다:
+
+- VQA 모델 중 하나인 [ViLT](../../en/model_doc/vilt)를 [`Graphcore/vqa` 데이터셋](https://huggingface.co/datasets/Graphcore/vqa) 에서 미세조정하는 방법
+- 미세조정된 ViLT 모델로 추론하는 방법
+- BLIP-2 같은 생성 모델로 제로샷 VQA 추론을 실행하는 방법
+
+## ViLT 미세 조정 [[finetuning-vilt]]
+
+ViLT는 Vision Transformer (ViT) 내에 텍스트 임베딩을 포함하여 비전/자연어 사전훈련(VLP; Vision-and-Language Pretraining)을 위한 기본 디자인을 제공합니다.
+ViLT 모델은 비전 트랜스포머(ViT)에 텍스트 임베딩을 넣어 비전/언어 사전훈련(VLP; Vision-and-Language Pre-training)을 위한 기본적인 디자인을 갖췄습니다. 이 모델은 여러 다운스트림 작업에 사용할 수 있습니다. VQA 태스크에서는 (`[CLS]` 토큰의 최종 은닉 상태 위에 선형 레이어인) 분류 헤더가 있으며 무작위로 초기화됩니다.
+따라서 여기에서 시각적 질의응답은 **분류 문제**로 취급됩니다.
+
+최근의 BLIP, BLIP-2, InstructBLIP와 같은 모델들은 VQA를 생성형 작업으로 간주합니다. 가이드의 후반부에서는 이런 모델들을 사용하여 제로샷 VQA 추론을 하는 방법에 대해 설명하겠습니다.
+
+시작하기 전 필요한 모든 라이브러리를 설치했는지 확인하세요.
+
+```bash
+pip install -q transformers datasets
+```
+
+커뮤니티에 모델을 공유하는 것을 권장 드립니다. Hugging Face 계정에 로그인하여 🤗 Hub에 업로드할 수 있습니다.
+메시지가 나타나면 로그인할 토큰을 입력하세요:
+
+```py
+>>> from huggingface_hub import notebook_login
+
+>>> notebook_login()
+```
+
+모델 체크포인트를 전역 변수로 선언하세요.
+
+```py
+>>> model_checkpoint = "dandelin/vilt-b32-mlm"
+```
+
+## 데이터 가져오기 [[load-the-data]]
+
+이 가이드에서는 `Graphcore/vqa` 데이터세트의 작은 샘플을 사용합니다. 전체 데이터세트는 [🤗 Hub](https://huggingface.co/datasets/Graphcore/vqa) 에서 확인할 수 있습니다.
+
+[`Graphcore/vqa` 데이터세트](https://huggingface.co/datasets/Graphcore/vqa) 의 대안으로 공식 [VQA 데이터세트 페이지](https://visualqa.org/download.html) 에서 동일한 데이터를 수동으로 다운로드할 수 있습니다. 직접 공수한 데이터로 튜토리얼을 따르고 싶다면 [이미지 데이터세트 만들기](https://huggingface.co/docs/datasets/image_dataset#loading-script) 라는
+🤗 Datasets 문서를 참조하세요.
+
+검증 데이터의 첫 200개 항목을 불러와 데이터세트의 특성을 확인해 보겠습니다:
+
+```python
+>>> from datasets import load_dataset
+
+>>> dataset = load_dataset("Graphcore/vqa", split="validation[:200]")
+>>> dataset
+Dataset({
+ features: ['question', 'question_type', 'question_id', 'image_id', 'answer_type', 'label'],
+ num_rows: 200
+})
+```
+
+예제를 하나 뽑아 데이터세트의 특성을 이해해 보겠습니다.
+
+```py
+>>> dataset[0]
+{'question': 'Where is he looking?',
+ 'question_type': 'none of the above',
+ 'question_id': 262148000,
+ 'image_id': '/root/.cache/huggingface/datasets/downloads/extracted/ca733e0e000fb2d7a09fbcc94dbfe7b5a30750681d0e965f8e0a23b1c2f98c75/val2014/COCO_val2014_000000262148.jpg',
+ 'answer_type': 'other',
+ 'label': {'ids': ['at table', 'down', 'skateboard', 'table'],
+ 'weights': [0.30000001192092896,
+ 1.0,
+ 0.30000001192092896,
+ 0.30000001192092896]}}
+```
+
+데이터세트에는 다음과 같은 특성이 포함되어 있습니다:
+* `question`: 이미지에 대한 질문
+* `image_id`: 질문과 관련된 이미지의 경로
+* `label`: 데이터의 레이블 (annotations)
+
+나머지 특성들은 필요하지 않기 때문에 삭제해도 됩니다:
+
+```py
+>>> dataset = dataset.remove_columns(['question_type', 'question_id', 'answer_type'])
+```
+
+보시다시피 `label` 특성은 같은 질문마다 답변이 여러 개 있을 수 있습니다. 모두 다른 데이터 라벨러들로부터 수집되었기 때문인데요. 질문의 답변은 주관적일 수 있습니다. 이 경우 질문은 "그는 어디를 보고 있나요?" 였지만, 어떤 사람들은 "아래"로 레이블을 달았고, 다른 사람들은 "테이블" 또는 "스케이트보드" 등으로 주석을 달았습니다.
+
+아래의 이미지를 보고 어떤 답변을 선택할 것인지 생각해 보세요:
+
+```python
+>>> from PIL import Image
+
+>>> image = Image.open(dataset[0]['image_id'])
+>>> image
+```
+
+
+
+ 
+
+
+질문과 답변의 모호성으로 인해 이러한 데이터세트는 여러 개의 답변이 가능하므로 다중 레이블 분류 문제로 처리됩니다. 게다가, 원핫(one-hot) 인코딩 벡터를 생성하기보다는 레이블에서 특정 답변이 나타나는 횟수를 기반으로 소프트 인코딩을 생성합니다.
+
+위의 예시에서 "아래"라는 답변이 다른 답변보다 훨씬 더 자주 선택되었기 때문에 데이터세트에서 `weight`라고 불리는 점수로 1.0을 가지며, 나머지 답변들은 1.0 미만의 점수를 가집니다.
+
+적절한 분류 헤더로 모델을 나중에 인스턴스화하기 위해 레이블을 정수로 매핑한 딕셔너리 하나, 반대로 정수를 레이블로 매핑한 딕셔너리 하나 총 2개의 딕셔너리를 생성하세요:
+
+```py
+>>> import itertools
+
+>>> labels = [item['ids'] for item in dataset['label']]
+>>> flattened_labels = list(itertools.chain(*labels))
+>>> unique_labels = list(set(flattened_labels))
+
+>>> label2id = {label: idx for idx, label in enumerate(unique_labels)}
+>>> id2label = {idx: label for label, idx in label2id.items()}
+```
+
+이제 매핑이 완료되었으므로 문자열 답변을 해당 id로 교체하고, 데이터세트의 더 편리한 후처리를 위해 편평화 할 수 있습니다.
+
+```python
+>>> def replace_ids(inputs):
+... inputs["label"]["ids"] = [label2id[x] for x in inputs["label"]["ids"]]
+... return inputs
+
+
+>>> dataset = dataset.map(replace_ids)
+>>> flat_dataset = dataset.flatten()
+>>> flat_dataset.features
+{'question': Value(dtype='string', id=None),
+ 'image_id': Value(dtype='string', id=None),
+ 'label.ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None),
+ 'label.weights': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None)}
+```
+
+## 데이터 전처리 [[preprocessing-data]]
+
+다음 단계는 모델을 위해 이미지와 텍스트 데이터를 준비하기 위해 ViLT 프로세서를 가져오는 것입니다.
+[`ViltProcessor`]는 BERT 토크나이저와 ViLT 이미지 프로세서를 편리하게 하나의 프로세서로 묶습니다:
+
+```py
+>>> from transformers import ViltProcessor
+
+>>> processor = ViltProcessor.from_pretrained(model_checkpoint)
+```
+
+데이터를 전처리하려면 이미지와 질문을 [`ViltProcessor`]로 인코딩해야 합니다. 프로세서는 [`BertTokenizerFast`]로 텍스트를 토크나이즈하고 텍스트 데이터를 위해 `input_ids`, `attention_mask` 및 `token_type_ids`를 생성합니다.
+이미지는 [`ViltImageProcessor`]로 이미지를 크기 조정하고 정규화하며, `pixel_values`와 `pixel_mask`를 생성합니다.
+
+이런 전처리 단계는 모두 내부에서 이루어지므로, `processor`를 호출하기만 하면 됩니다. 하지만 아직 타겟 레이블이 완성되지 않았습니다. 타겟의 표현에서 각 요소는 가능한 답변(레이블)에 해당합니다. 정확한 답변의 요소는 해당 점수(weight)를 유지시키고 나머지 요소는 0으로 설정해야 합니다.
+
+아래 함수가 위에서 설명한대로 이미지와 질문에 `processor`를 적용하고 레이블을 형식에 맞춥니다:
+
+```py
+>>> import torch
+
+>>> def preprocess_data(examples):
+... image_paths = examples['image_id']
+... images = [Image.open(image_path) for image_path in image_paths]
+... texts = examples['question']
+
+... encoding = processor(images, texts, padding="max_length", truncation=True, return_tensors="pt")
+
+... for k, v in encoding.items():
+... encoding[k] = v.squeeze()
+
+... targets = []
+
+... for labels, scores in zip(examples['label.ids'], examples['label.weights']):
+... target = torch.zeros(len(id2label))
+
+... for label, score in zip(labels, scores):
+... target[label] = score
+
+... targets.append(target)
+
+... encoding["labels"] = targets
+
+... return encoding
+```
+
+전체 데이터세트에 전처리 함수를 적용하려면 🤗 Datasets의 [`~datasets.map`] 함수를 사용하십시오. `batched=True`를 설정하여 데이터세트의 여러 요소를 한 번에 처리함으로써 `map`을 더 빠르게 할 수 있습니다. 이 시점에서 필요하지 않은 열은 제거하세요.
+
+```py
+>>> processed_dataset = flat_dataset.map(preprocess_data, batched=True, remove_columns=['question','question_type', 'question_id', 'image_id', 'answer_type', 'label.ids', 'label.weights'])
+>>> processed_dataset
+Dataset({
+ features: ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values', 'pixel_mask', 'labels'],
+ num_rows: 200
+})
+```
+
+마지막 단계로, [`DefaultDataCollator`]를 사용하여 예제로 쓸 배치를 생성하세요:
+
+```py
+>>> from transformers import DefaultDataCollator
+
+>>> data_collator = DefaultDataCollator()
+```
+
+## 모델 훈련 [[train-the-model]]
+
+이제 모델을 훈련하기 위해 준비되었습니다! [`ViltForQuestionAnswering`]으로 ViLT를 가져올 차례입니다. 레이블의 수와 레이블 매핑을 지정하세요:
+
+```py
+>>> from transformers import ViltForQuestionAnswering
+
+>>> model = ViltForQuestionAnswering.from_pretrained(model_checkpoint, num_labels=len(id2label), id2label=id2label, label2id=label2id)
+```
+
+이 시점에서는 다음 세 단계만 남았습니다:
+
+1. [`TrainingArguments`]에서 훈련 하이퍼파라미터를 정의하세요:
+
+```py
+>>> from transformers import TrainingArguments
+
+>>> repo_id = "MariaK/vilt_finetuned_200"
+
+>>> training_args = TrainingArguments(
+... output_dir=repo_id,
+... per_device_train_batch_size=4,
+... num_train_epochs=20,
+... save_steps=200,
+... logging_steps=50,
+... learning_rate=5e-5,
+... save_total_limit=2,
+... remove_unused_columns=False,
+... push_to_hub=True,
+... )
+```
+
+2. 모델, 데이터세트, 프로세서, 데이터 콜레이터와 함께 훈련 인수를 [`Trainer`]에 전달하세요:
+
+```py
+>>> from transformers import Trainer
+
+>>> trainer = Trainer(
+... model=model,
+... args=training_args,
+... data_collator=data_collator,
+... train_dataset=processed_dataset,
+... processing_class=processor,
+... )
+```
+
+3. [`~Trainer.train`]을 호출하여 모델을 미세 조정하세요:
+
+```py
+>>> trainer.train()
+```
+
+훈련이 완료되면, [`~Trainer.push_to_hub`] 메소드를 사용하여 🤗 Hub에 모델을 공유하세요:
+
+```py
+>>> trainer.push_to_hub()
+```
+
+## 추론 [[inference]]
+
+ViLT 모델을 미세 조정하고 🤗 Hub에 업로드했다면 추론에 사용할 수 있습니다. 미세 조정된 모델을 추론에 사용해보는 가장 간단한 방법은 [`Pipeline`]에서 사용하는 것입니다.
+
+```py
+>>> from transformers import pipeline
+
+>>> pipe = pipeline("visual-question-answering", model="MariaK/vilt_finetuned_200")
+```
+
+이 가이드의 모델은 200개의 예제에서만 훈련되었으므로 그다지 많은 것을 기대할 수는 없습니다. 데이터세트의 첫 번째 예제를 사용하여 추론 결과를 설명해보겠습니다:
+
+```py
+>>> example = dataset[0]
+>>> image = Image.open(example['image_id'])
+>>> question = example['question']
+>>> print(question)
+>>> pipe(image, question, top_k=1)
+"Where is he looking?"
+[{'score': 0.5498199462890625, 'answer': 'down'}]
+```
+
+비록 확신은 별로 없지만, 모델은 실제로 무언가를 배웠습니다. 더 많은 예제와 더 긴 훈련 기간이 주어진다면 분명 더 나은 결과를 얻을 수 있을 것입니다!
+
+원한다면 파이프라인의 결과를 수동으로 복제할 수도 있습니다:
+1. 이미지와 질문을 가져와서 프로세서를 사용하여 모델에 준비합니다.
+2. 전처리된 결과를 모델에 전달합니다.
+3. 로짓에서 가장 가능성 있는 답변의 id를 가져와서 `id2label`에서 실제 답변을 찾습니다.
+
+```py
+>>> processor = ViltProcessor.from_pretrained("MariaK/vilt_finetuned_200")
+
+>>> image = Image.open(example['image_id'])
+>>> question = example['question']
+
+>>> # prepare inputs
+>>> inputs = processor(image, question, return_tensors="pt")
+
+>>> model = ViltForQuestionAnswering.from_pretrained("MariaK/vilt_finetuned_200")
+
+>>> # forward pass
+>>> with torch.no_grad():
+... outputs = model(**inputs)
+
+>>> logits = outputs.logits
+>>> idx = logits.argmax(-1).item()
+>>> print("Predicted answer:", model.config.id2label[idx])
+Predicted answer: down
+```
+
+## 제로샷 VQA [[zeroshot-vqa]]
+
+이전 모델은 VQA를 분류 문제로 처리했습니다. BLIP, BLIP-2 및 InstructBLIP와 같은 최근의 모델은 VQA를 생성 작업으로 접근합니다. [BLIP-2](../../en/model_doc/blip-2)를 예로 들어 보겠습니다. 이 모델은 사전훈련된 비전 인코더와 LLM의 모든 조합을 사용할 수 있는 새로운 비전-자연어 사전 학습 패러다임을 도입했습니다. ([BLIP-2 블로그 포스트](https://huggingface.co/blog/blip-2)를 통해 더 자세히 알아볼 수 있어요)
+이를 통해 시각적 질의응답을 포함한 여러 비전-자연어 작업에서 SOTA를 달성할 수 있었습니다.
+
+이 모델을 어떻게 VQA에 사용할 수 있는지 설명해 보겠습니다. 먼저 모델을 가져와 보겠습니다. 여기서 GPU가 사용 가능한 경우 모델을 명시적으로 GPU로 전송할 것입니다. 이전에는 훈련할 때 쓰지 않은 이유는 [`Trainer`]가 이 부분을 자동으로 처리하기 때문입니다:
+
+```py
+>>> from transformers import AutoProcessor, Blip2ForConditionalGeneration
+>>> import torch
+
+>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
+>>> model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
+>>> device = "cuda" if torch.cuda.is_available() else "cpu"
+>>> model.to(device)
+```
+
+모델은 이미지와 텍스트를 입력으로 받으므로, VQA 데이터세트의 첫 번째 예제에서와 동일한 이미지/질문 쌍을 사용해 보겠습니다:
+
+```py
+>>> example = dataset[0]
+>>> image = Image.open(example['image_id'])
+>>> question = example['question']
+```
+
+BLIP-2를 시각적 질의응답 작업에 사용하려면 텍스트 프롬프트가 `Question: {} Answer:` 형식을 따라야 합니다.
+
+```py
+>>> prompt = f"Question: {question} Answer:"
+```
+
+이제 모델의 프로세서로 이미지/프롬프트를 전처리하고, 처리된 입력을 모델을 통해 전달하고, 출력을 디코드해야 합니다:
+
+```py
+>>> inputs = processor(image, text=prompt, return_tensors="pt").to(device, torch.float16)
+
+>>> generated_ids = model.generate(**inputs, max_new_tokens=10)
+>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
+>>> print(generated_text)
+"He is looking at the crowd"
+```
+
+보시다시피 모델은 군중을 인식하고, 얼굴의 방향(아래쪽을 보고 있음)을 인식했지만, 군중이 스케이터 뒤에 있다는 사실을 놓쳤습니다. 그러나 사람이 직접 라벨링한 데이터셋을 얻을 수 없는 경우에, 이 접근법은 빠르게 유용한 결과를 생성할 수 있습니다.
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/zero_shot_image_classification.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/zero_shot_image_classification.md
new file mode 100644
index 0000000000000000000000000000000000000000..f824de93b8652201f72e9b78aa8b2ff634047cb8
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/zero_shot_image_classification.md
@@ -0,0 +1,144 @@
+
+
+# 제로샷(zero-shot) 이미지 분류[[zeroshot-image-classification]]
+
+[[open-in-colab]]
+
+제로샷(zero-shot) 이미지 분류는 특정 카테고리의 예시가 포함된 데이터를 학습되지 않은 모델을 사용해 이미지 분류를 수행하는 작업입니다.
+
+일반적으로 이미지 분류를 위해서는 레이블이 달린 특정 이미지 데이터로 모델 학습이 필요하며, 이 모델은 특정 이미지의 특징을 레이블에 "매핑"하는 방법을 학습합니다.
+새로운 레이블이 있는 분류 작업에 이러한 모델을 사용해야 하는 경우에는, 모델을 "재보정"하기 위해 미세 조정이 필요합니다.
+
+이와 대조적으로, 제로샷 또는 개방형 어휘(open vocabulary) 이미지 분류 모델은 일반적으로 대규모 이미지 데이터와 해당 설명에 대해 학습된 멀티모달(multimodal) 모델입니다.
+이러한 모델은 제로샷 이미지 분류를 포함한 많은 다운스트림 작업에 사용할 수 있는 정렬된(aligned) 비전 언어 표현을 학습합니다.
+
+이는 이미지 분류에 대한 보다 유연한 접근 방식으로, 추가 학습 데이터 없이 새로운 레이블이나 학습하지 못한 카테고리에 대해 모델을 일반화할 수 있습니다.
+또한, 사용자가 대상 개체에 대한 자유 형식의 텍스트 설명으로 이미지를 검색할 수 있습니다.
+
+이번 가이드에서 배울 내용은 다음과 같습니다:
+
+* 제로샷 이미지 분류 파이프라인 만들기
+* 직접 제로샷 이미지 분류 모델 추론 실행하기
+
+시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요:
+
+```bash
+pip install -q transformers
+```
+
+## 제로샷(zero-shot) 이미지 분류 파이프라인[[zeroshot-image-classification-pipeline]]
+
+[`pipeline`]을 활용하면 가장 간단하게 제로샷 이미지 분류를 지원하는 모델로 추론해볼 수 있습니다.
+[Hugging Face Hub에 업로드된 체크포인트](https://huggingface.co/models?pipeline_tag=zero-shot-image-classification&sort=downloads)에서 파이프라인을 인스턴스화합니다.
+
+```python
+>>> from transformers import pipeline
+
+>>> checkpoint = "openai/clip-vit-large-patch14"
+>>> detector = pipeline(model=checkpoint, task="zero-shot-image-classification")
+```
+
+다음으로, 분류하고 싶은 이미지를 선택하세요.
+
+```py
+>>> from PIL import Image
+>>> import requests
+
+>>> url = "https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&force=true&w=640"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+
+>>> image
+```
+
+
+
+ 
+
+
+이미지와 해당 이미지의 후보 레이블인 `candidate_labels`를 파이프라인으로 전달합니다.
+여기서는 이미지를 직접 전달하지만, 컴퓨터에 저장된 이미지의 경로나 url로 전달할 수도 있습니다.
+`candidate_labels`는 이 예시처럼 간단한 단어일 수도 있고 좀 더 설명적인 단어일 수도 있습니다.
+
+```py
+>>> predictions = classifier(image, candidate_labels=["fox", "bear", "seagull", "owl"])
+>>> predictions
+[{'score': 0.9996670484542847, 'label': 'owl'},
+ {'score': 0.000199399160919711, 'label': 'seagull'},
+ {'score': 7.392891711788252e-05, 'label': 'fox'},
+ {'score': 5.96074532950297e-05, 'label': 'bear'}]
+```
+
+## 직접 제로샷(zero-shot) 이미지 분류하기[[zeroshot-image-classification-by-hand]]
+
+이제 제로샷 이미지 분류 파이프라인 사용 방법을 살펴보았으니, 실행하는 방법을 살펴보겠습니다.
+
+[Hugging Face Hub에 업로드된 체크포인트](https://huggingface.co/models?pipeline_tag=zero-shot-image-classification&sort=downloads)에서 모델과 프로세서를 가져오는 것으로 시작합니다.
+여기서는 이전과 동일한 체크포인트를 사용하겠습니다:
+
+```py
+>>> from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
+
+>>> model = AutoModelForZeroShotImageClassification.from_pretrained(checkpoint)
+>>> processor = AutoProcessor.from_pretrained(checkpoint)
+```
+
+다른 이미지를 사용해 보겠습니다.
+
+```py
+>>> from PIL import Image
+>>> import requests
+
+>>> url = "https://unsplash.com/photos/xBRQfR2bqNI/download?ixid=MnwxMjA3fDB8MXxhbGx8fHx8fHx8fHwxNjc4Mzg4ODEx&force=true&w=640"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+
+>>> image
+```
+
+
+
+ 
+
+
+프로세서를 사용해 모델의 입력을 준비합니다.
+프로세서는 모델의 입력으로 사용하기 위해 이미지 크기를 변환하고 정규화하는 이미지 프로세서와 텍스트 입력을 처리하는 토크나이저로 구성됩니다.
+
+```py
+>>> candidate_labels = ["tree", "car", "bike", "cat"]
+>>> inputs = processor(images=image, text=candidate_labels, return_tensors="pt", padding=True)
+```
+
+모델에 입력을 전달하고, 결과를 후처리합니다:
+
+```py
+>>> import torch
+
+>>> with torch.no_grad():
+... outputs = model(**inputs)
+
+>>> logits = outputs.logits_per_image[0]
+>>> probs = logits.softmax(dim=-1).numpy()
+>>> scores = probs.tolist()
+
+>>> result = [
+... {"score": score, "label": candidate_label}
+... for score, candidate_label in sorted(zip(probs, candidate_labels), key=lambda x: -x[0])
+... ]
+
+>>> result
+[{'score': 0.998572, 'label': 'car'},
+ {'score': 0.0010570387, 'label': 'bike'},
+ {'score': 0.0003393686, 'label': 'tree'},
+ {'score': 3.1572064e-05, 'label': 'cat'}]
+```
\ No newline at end of file
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/zero_shot_object_detection.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/zero_shot_object_detection.md
new file mode 100644
index 0000000000000000000000000000000000000000..8e9b52e8c7a20fd5e14b5c3288dcbf2e758f6294
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/tasks/zero_shot_object_detection.md
@@ -0,0 +1,307 @@
+
+
+# 제로샷(zero-shot) 객체 탐지[[zeroshot-object-detection]]
+
+[[open-in-colab]]
+
+일반적으로 [객체 탐지](object_detection)에 사용되는 모델을 학습하기 위해서는 레이블이 지정된 이미지 데이터 세트가 필요합니다.
+그리고 학습 데이터에 존재하는 클래스(레이블)만 탐지할 수 있다는 한계점이 있습니다.
+
+다른 방식을 사용하는 [OWL-ViT](../model_doc/owlvit) 모델로 제로샷 객체 탐지가 가능합니다.
+OWL-ViT는 개방형 어휘(open-vocabulary) 객체 탐지기입니다.
+즉, 레이블이 지정된 데이터 세트에 미세 조정하지 않고 자유 텍스트 쿼리를 기반으로 이미지에서 객체를 탐지할 수 있습니다.
+
+OWL-ViT 모델은 멀티 모달 표현을 활용해 개방형 어휘 탐지(open-vocabulary detection)를 수행합니다.
+[CLIP](../model_doc/clip) 모델에 경량화(lightweight)된 객체 분류와 지역화(localization) 헤드를 결합합니다.
+개방형 어휘 탐지는 CLIP의 텍스트 인코더로 free-text 쿼리를 임베딩하고, 객체 분류와 지역화 헤드의 입력으로 사용합니다.
+이미지와 해당 텍스트 설명을 연결하면 ViT가 이미지 패치(image patches)를 입력으로 처리합니다.
+OWL-ViT 모델의 저자들은 CLIP 모델을 처음부터 학습(scratch learning)한 후에, bipartite matching loss를 사용하여 표준 객체 인식 데이터셋으로 OWL-ViT 모델을 미세 조정했습니다.
+
+이 접근 방식을 사용하면 모델은 레이블이 지정된 데이터 세트에 대한 사전 학습 없이도 텍스트 설명을 기반으로 객체를 탐지할 수 있습니다.
+
+이번 가이드에서는 OWL-ViT 모델의 사용법을 다룰 것입니다:
+- 텍스트 프롬프트 기반 객체 탐지
+- 일괄 객체 탐지
+- 이미지 가이드 객체 탐지
+
+시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요:
+```bash
+pip install -q transformers
+```
+
+## 제로샷(zero-shot) 객체 탐지 파이프라인[[zeroshot-object-detection-pipeline]]
+
+[`pipeline`]을 활용하면 가장 간단하게 OWL-ViT 모델을 추론해볼 수 있습니다.
+[Hugging Face Hub에 업로드된 체크포인트](https://huggingface.co/models?pipeline_tag=zero-shot-image-classification&sort=downloads)에서 제로샷(zero-shot) 객체 탐지용 파이프라인을 인스턴스화합니다:
+
+```python
+>>> from transformers import pipeline
+
+>>> checkpoint = "google/owlvit-base-patch32"
+>>> detector = pipeline(model=checkpoint, task="zero-shot-object-detection")
+```
+
+다음으로, 객체를 탐지하고 싶은 이미지를 선택하세요.
+여기서는 [NASA](https://www.nasa.gov/multimedia/imagegallery/index.html) Great Images 데이터 세트의 일부인 우주비행사 에일린 콜린스(Eileen Collins) 사진을 사용하겠습니다.
+
+```py
+>>> import skimage
+>>> import numpy as np
+>>> from PIL import Image
+
+>>> image = skimage.data.astronaut()
+>>> image = Image.fromarray(np.uint8(image)).convert("RGB")
+
+>>> image
+```
+
+
+
+ 
+
+
+이미지와 해당 이미지의 후보 레이블을 파이프라인으로 전달합니다.
+여기서는 이미지를 직접 전달하지만, 컴퓨터에 저장된 이미지의 경로나 url로 전달할 수도 있습니다.
+candidate_labels는 이 예시처럼 간단한 단어일 수도 있고 좀 더 설명적인 단어일 수도 있습니다.
+또한, 이미지를 검색(query)하려는 모든 항목에 대한 텍스트 설명도 전달합니다.
+
+```py
+>>> predictions = detector(
+... image,
+... candidate_labels=["human face", "rocket", "nasa badge", "star-spangled banner"],
+... )
+>>> predictions
+[{'score': 0.3571370542049408,
+ 'label': 'human face',
+ 'box': {'xmin': 180, 'ymin': 71, 'xmax': 271, 'ymax': 178}},
+ {'score': 0.28099656105041504,
+ 'label': 'nasa badge',
+ 'box': {'xmin': 129, 'ymin': 348, 'xmax': 206, 'ymax': 427}},
+ {'score': 0.2110239565372467,
+ 'label': 'rocket',
+ 'box': {'xmin': 350, 'ymin': -1, 'xmax': 468, 'ymax': 288}},
+ {'score': 0.13790413737297058,
+ 'label': 'star-spangled banner',
+ 'box': {'xmin': 1, 'ymin': 1, 'xmax': 105, 'ymax': 509}},
+ {'score': 0.11950037628412247,
+ 'label': 'nasa badge',
+ 'box': {'xmin': 277, 'ymin': 338, 'xmax': 327, 'ymax': 380}},
+ {'score': 0.10649408400058746,
+ 'label': 'rocket',
+ 'box': {'xmin': 358, 'ymin': 64, 'xmax': 424, 'ymax': 280}}]
+```
+
+이제 예측값을 시각화해봅시다:
+
+```py
+>>> from PIL import ImageDraw
+
+>>> draw = ImageDraw.Draw(image)
+
+>>> for prediction in predictions:
+... box = prediction["box"]
+... label = prediction["label"]
+... score = prediction["score"]
+
+... xmin, ymin, xmax, ymax = box.values()
+... draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
+... draw.text((xmin, ymin), f"{label}: {round(score,2)}", fill="white")
+
+>>> image
+```
+
+
+
+ 
+
+
+## 텍스트 프롬프트 기반 객체 탐지[[textprompted-zeroshot-object-detection-by-hand]]
+
+제로샷 객체 탐지 파이프라인 사용법에 대해 살펴보았으니, 이제 동일한 결과를 복제해보겠습니다.
+
+[Hugging Face Hub에 업로드된 체크포인트](https://huggingface.co/models?other=owlvit)에서 관련 모델과 프로세서를 가져오는 것으로 시작합니다.
+여기서는 이전과 동일한 체크포인트를 사용하겠습니다:
+
+```py
+>>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+
+>>> model = AutoModelForZeroShotObjectDetection.from_pretrained(checkpoint)
+>>> processor = AutoProcessor.from_pretrained(checkpoint)
+```
+
+다른 이미지를 사용해 보겠습니다:
+
+```py
+>>> import requests
+
+>>> url = "https://unsplash.com/photos/oj0zeY2Ltk4/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MTR8fHBpY25pY3xlbnwwfHx8fDE2Nzc0OTE1NDk&force=true&w=640"
+>>> im = Image.open(requests.get(url, stream=True).raw)
+>>> im
+```
+
+
+
+ 
+
+
+프로세서를 사용해 모델의 입력을 준비합니다.
+프로세서는 모델의 입력으로 사용하기 위해 이미지 크기를 변환하고 정규화하는 이미지 프로세서와 텍스트 입력을 처리하는 [`CLIPTokenizer`]로 구성됩니다.
+
+```py
+>>> text_queries = ["hat", "book", "sunglasses", "camera"]
+>>> inputs = processor(text=text_queries, images=im, return_tensors="pt")
+```
+
+모델에 입력을 전달하고 결과를 후처리 및 시각화합니다.
+이미지 프로세서가 모델에 이미지를 입력하기 전에 이미지 크기를 조정했기 때문에, [`~OwlViTImageProcessor.post_process_object_detection`] 메소드를 사용해
+예측값의 바운딩 박스(bounding box)가 원본 이미지의 좌표와 상대적으로 동일한지 확인해야 합니다.
+
+```py
+>>> import torch
+
+>>> with torch.no_grad():
+... outputs = model(**inputs)
+... target_sizes = torch.tensor([im.size[::-1]])
+... results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes)[0]
+
+>>> draw = ImageDraw.Draw(im)
+
+>>> scores = results["scores"].tolist()
+>>> labels = results["labels"].tolist()
+>>> boxes = results["boxes"].tolist()
+
+>>> for box, score, label in zip(boxes, scores, labels):
+... xmin, ymin, xmax, ymax = box
+... draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
+... draw.text((xmin, ymin), f"{text_queries[label]}: {round(score,2)}", fill="white")
+
+>>> im
+```
+
+
+
+ 
+
+
+## 일괄 처리[[batch-processing]]
+
+여러 이미지와 텍스트 쿼리를 전달하여 여러 이미지에서 서로 다른(또는 동일한) 객체를 검색할 수 있습니다.
+일괄 처리를 위해서 텍스트 쿼리는 이중 리스트로, 이미지는 PIL 이미지, PyTorch 텐서, 또는 NumPy 배열로 이루어진 리스트로 프로세서에 전달해야 합니다.
+
+```py
+>>> images = [image, im]
+>>> text_queries = [
+... ["human face", "rocket", "nasa badge", "star-spangled banner"],
+... ["hat", "book", "sunglasses", "camera"],
+... ]
+>>> inputs = processor(text=text_queries, images=images, return_tensors="pt")
+```
+
+이전에는 후처리를 위해 단일 이미지의 크기를 텐서로 전달했지만, 튜플을 전달할 수 있고, 여러 이미지를 처리하는 경우에는 튜플로 이루어진 리스트를 전달할 수도 있습니다.
+아래 두 예제에 대한 예측을 생성하고, 두 번째 이미지(`image_idx = 1`)를 시각화해 보겠습니다.
+
+```py
+>>> with torch.no_grad():
+... outputs = model(**inputs)
+... target_sizes = [x.size[::-1] for x in images]
+... results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes)
+
+>>> image_idx = 1
+>>> draw = ImageDraw.Draw(images[image_idx])
+
+>>> scores = results[image_idx]["scores"].tolist()
+>>> labels = results[image_idx]["labels"].tolist()
+>>> boxes = results[image_idx]["boxes"].tolist()
+
+>>> for box, score, label in zip(boxes, scores, labels):
+... xmin, ymin, xmax, ymax = box
+... draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
+... draw.text((xmin, ymin), f"{text_queries[image_idx][label]}: {round(score,2)}", fill="white")
+
+>>> images[image_idx]
+```
+
+
+
+
+
+
+## 이미지 가이드 객체 탐지[[imageguided-object-detection]]
+
+텍스트 쿼리를 이용한 제로샷 객체 탐지 외에도 OWL-ViT 모델은 이미지 가이드 객체 탐지 기능을 제공합니다.
+이미지를 쿼리로 사용해 대상 이미지에서 유사한 객체를 찾을 수 있다는 의미입니다.
+텍스트 쿼리와 달리 하나의 예제 이미지에서만 가능합니다.
+
+소파에 고양이 두 마리가 있는 이미지를 대상 이미지(target image)로, 고양이 한 마리가 있는 이미지를 쿼리로 사용해보겠습니다:
+
+```py
+>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+>>> image_target = Image.open(requests.get(url, stream=True).raw)
+
+>>> query_url = "http://images.cocodataset.org/val2017/000000524280.jpg"
+>>> query_image = Image.open(requests.get(query_url, stream=True).raw)
+```
+
+다음 이미지를 살펴보겠습니다:
+
+```py
+>>> import matplotlib.pyplot as plt
+
+>>> fig, ax = plt.subplots(1, 2)
+>>> ax[0].imshow(image_target)
+>>> ax[1].imshow(query_image)
+```
+
+
+
+ 
+
+
+전처리 단계에서 텍스트 쿼리 대신에 `query_images`를 사용합니다:
+
+```py
+>>> inputs = processor(images=image_target, query_images=query_image, return_tensors="pt")
+```
+
+예측의 경우, 모델에 입력을 전달하는 대신 [`~OwlViTForObjectDetection.image_guided_detection`]에 전달합니다.
+레이블이 없다는 점을 제외하면 이전과 동일합니다.
+이전과 동일하게 이미지를 시각화합니다.
+
+```py
+>>> with torch.no_grad():
+... outputs = model.image_guided_detection(**inputs)
+... target_sizes = torch.tensor([image_target.size[::-1]])
+... results = processor.post_process_image_guided_detection(outputs=outputs, target_sizes=target_sizes)[0]
+
+>>> draw = ImageDraw.Draw(image_target)
+
+>>> scores = results["scores"].tolist()
+>>> boxes = results["boxes"].tolist()
+
+>>> for box, score, label in zip(boxes, scores, labels):
+... xmin, ymin, xmax, ymax = box
+... draw.rectangle((xmin, ymin, xmax, ymax), outline="white", width=4)
+
+>>> image_target
+```
+
+
+
+ 
+
+
+OWL-ViT 모델을 추론하고 싶다면 아래 데모를 확인하세요:
+
+
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/testing.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/testing.md
new file mode 100644
index 0000000000000000000000000000000000000000..0a9e8ee47acad0a1860e15ead6cb9c8acc4a80a9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/ko/testing.md
@@ -0,0 +1,1271 @@
+
+
+# 테스트[[testing]]
+
+
+먼저 🤗 Transformers 모델이 어떻게 테스트되는지 살펴보고, 새로운 테스트를 작성 및 기존 테스트를 개선하는 방법을 알아봅시다.
+
+이 저장소에는 2개의 테스트 스위트가 있습니다:
+
+1. `tests` - 일반 API에 대한 테스트
+2. `examples` - API의 일부가 아닌 다양한 응용 프로그램에 대한 테스트
+
+## Transformers 테스트 방법[[how-transformers-are-tested]]
+
+1. PR이 제출되면 9개의 CircleCi 작업으로 테스트가 진행됩니다. 해당 PR에 대해 새로운 커밋이 생성될 때마다 테스트는 다시 진행됩니다. 이 작업들은
+ 이 [config 파일](https://github.com/huggingface/transformers/tree/main/.circleci/config.yml)에 정의되어 있으므로 필요하다면
+ 사용자의 로컬 환경에서 동일하게 재현해 볼 수 있습니다.
+
+ 이 CI 작업은 `@slow` 테스트를 실행하지 않습니다.
+
+2. [github actions](https://github.com/huggingface/transformers/actions)에 의해 실행되는 작업은 3개입니다:
+
+ - [torch hub integration](https://github.com/huggingface/transformers/tree/main/.github/workflows/github-torch-hub.yml):
+ torch hub integration이 작동하는지 확인합니다.
+
+ - [self-hosted (push)](https://github.com/huggingface/transformers/tree/main/.github/workflows/self-push.yml): `main` 브랜치에서 커밋이 업데이트된 경우에만 GPU를 이용한 빠른 테스트를 실행합니다.
+ 이는 `src`, `tests`, `.github` 폴더 중 하나에 코드가 업데이트된 경우에만 실행됩니다.
+ (model card, notebook, 기타 등등을 추가한 경우 실행되지 않도록 하기 위해서입니다)
+
+ - [self-hosted runner](https://github.com/huggingface/transformers/tree/main/.github/workflows/self-scheduled.yml): `tests` 및 `examples`에서
+ GPU를 이용한 일반 테스트, 느린 테스트를 실행합니다.
+
+
+```bash
+RUN_SLOW=1 pytest tests/
+RUN_SLOW=1 pytest examples/
+```
+
+ 결과는 [여기](https://github.com/huggingface/transformers/actions)에서 확인할 수 있습니다.
+
+
+## 테스트 실행[[running-tests]]
+
+
+
+
+
+### 실행할 테스트 선택[[choosing-which-tests-to-run]]
+
+이 문서는 테스트를 실행하는 다양한 방법에 대해 자세히 설명합니다.
+모든 내용을 읽은 후에도, 더 자세한 내용이 필요하다면 [여기](https://docs.pytest.org/en/latest/usage.html)에서 확인할 수 있습니다.
+
+다음은 가장 유용한 테스트 실행 방법 몇 가지입니다.
+
+모두 실행:
+
+```console
+pytest
+```
+
+또는:
+
+```bash
+make test
+```
+
+후자는 다음과 같이 정의됩니다:
+
+```bash
+python -m pytest -n auto --dist=loadfile -s -v ./tests/
+```
+
+위의 명령어는 pytest에게 아래의 내용을 전달합니다:
+
+- 사용 가능한 CPU 코어 수만큼 테스트 프로세스를 실행합니다. (RAM이 충분하지 않다면, 테스트 프로세스 수가 너무 많을 수 있습니다!)
+- 동일한 파일의 모든 테스트는 동일한 테스트 프로세스에서 실행되어야 합니다.
+- 출력을 캡처하지 않습니다.
+- 자세한 모드로 실행합니다.
+
+
+
+### 모든 테스트 목록 가져오기[[getting-the-list-of-all-tests]]
+
+테스트 스위트의 모든 테스트:
+
+```bash
+pytest --collect-only -q
+```
+
+지정된 테스트 파일의 모든 테스트:
+
+```bash
+pytest tests/test_optimization.py --collect-only -q
+```
+
+### 특정 테스트 모듈 실행[[run-a-specific-test-module]]
+
+개별 테스트 모듈 실행하기:
+
+```bash
+pytest tests/utils/test_logging.py
+```
+
+### 특정 테스트 실행[[run-specific-tests]]
+
+대부분의 테스트 내부에서는 unittest가 사용됩니다. 따라서 특정 하위 테스트를 실행하려면 해당 테스트를 포함하는 unittest 클래스의 이름을 알아야 합니다.
+예를 들어 다음과 같을 수 있습니다:
+
+```bash
+pytest tests/test_optimization.py::OptimizationTest::test_adam_w
+```
+
+위의 명령어의 의미는 다음과 같습니다:
+
+- `tests/test_optimization.py` - 테스트가 있는 파일
+- `OptimizationTest` - 클래스의 이름
+- `test_adam_w` - 특정 테스트 함수의 이름
+
+파일에 여러 클래스가 포함된 경우, 특정 클래스의 테스트만 실행할 수도 있습니다. 예를 들어 다음과 같습니다:
+
+```bash
+pytest tests/test_optimization.py::OptimizationTest
+```
+
+이 명령어는 해당 클래스 내부의 모든 테스트를 실행합니다.
+
+앞에서 언급한 것처럼 `OptimizationTest` 클래스에 포함된 테스트를 확인할 수 있습니다.
+
+```bash
+pytest tests/test_optimization.py::OptimizationTest --collect-only -q
+```
+
+키워드 표현식을 사용하여 테스트를 실행할 수도 있습니다.
+
+`adam`이라는 이름을 포함하는 테스트만 실행하려면 다음과 같습니다:
+
+```bash
+pytest -k adam tests/test_optimization.py
+```
+
+논리 연산자 `and`와 `or`를 사용하여 모든 키워드가 일치해야 하는지 또는 어느 하나가 일치해야 하는지를 나타낼 수 있습니다.
+`not`은 부정할 때 사용할 수 있습니다.
+
+`adam`이라는 이름을 포함하지 않는 모든 테스트를 실행하려면 다음과 같습니다:
+
+```bash
+pytest -k "not adam" tests/test_optimization.py
+```
+
+두 가지 패턴을 하나로 결합할 수도 있습니다:
+
+```bash
+pytest -k "ada and not adam" tests/test_optimization.py
+```
+
+예를 들어 `test_adafactor`와 `test_adam_w`를 모두 실행하려면 다음을 사용할 수 있습니다:
+
+```bash
+pytest -k "test_adam_w or test_adam_w" tests/test_optimization.py
+```
+
+여기서 `or`를 사용하는 것에 유의하세요. 두 키워드 중 하나가 일치하도록 하기 위한 목적으로 사용하기 때문입니다.
+
+두 패턴이 모두 포함되어야 하는 테스트만 실행하려면, `and`를 사용해야 합니다:
+
+```bash
+pytest -k "test and ada" tests/test_optimization.py
+```
+
+### `accelerate` 테스트 실행[[run-`accelerate`-tests]]
+
+모델에서 `accelerate` 테스트를 실행해야 할 때가 있습니다. 이를 위해서는 명령어에 `-m accelerate_tests`를 추가하면 됩니다.
+예를 들어, `OPT`에서 이러한 테스트를 실행하려면 다음과 같습니다:
+```bash
+RUN_SLOW=1 pytest -m accelerate_tests tests/models/opt/test_modeling_opt.py
+```
+
+### 문서 테스트 실행[[run-documentation-tests]]
+
+예시 문서가 올바른지 테스트하려면 `doctests`가 통과하는지 확인해야 합니다.
+예를 들어, [`WhisperModel.forward`'s docstring](https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py#L1017-L1035)를 사용해 봅시다:
+
+```python
+r"""
+Returns:
+
+Example:
+ ```python
+ >>> import torch
+ >>> from transformers import WhisperModel, WhisperFeatureExtractor
+ >>> from datasets import load_dataset
+
+ >>> model = WhisperModel.from_pretrained("openai/whisper-base")
+ >>> feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-base")
+ >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+ >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
+ >>> input_features = inputs.input_features
+ >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
+ >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
+ >>> list(last_hidden_state.shape)
+ [1, 2, 512]
+ ```"""
+
+```
+
+원하는 파일의 모든 docstring 예제를 자동으로 테스트하려면 다음 명령을 실행하면 됩니다:
+```bash
+pytest --doctest-modules
++ +이들 다른 [`Trainer`] 유형 클래스에 대해 더 알고 싶다면 [API 참조](./main_classes/trainer)를 확인하여 언제 어떤 클래스가 적합할지 얼마든지 확인하세요. 일반적으로 [`Trainer`]는 가장 다재다능한 옵션으로, 다양한 작업에 적합합니다. [`Seq2SeqTrainer`]는 시퀀스-투-시퀀스 작업을 위해 설계되었고, [`~trl.SFTTrainer`]는 언어 모델 훈련을 위해 설계되었습니다. + +
+🖼️ **Computer Vision**: pengelasan imej, pengesanan objek dan pembahagian.
+🗣️ **Audio**: pengecaman pertuturan automatik dan klasifikasi audio.
+🐙 **Multimodal**: jawapan soalan jadual, pengecaman aksara optik, pengekstrakan maklumat daripada dokumen yang diimbas, klasifikasi video dan jawapan soalan visual. + +🤗 Transformer menyokong kebolehoperasian rangka kerja antara PyTorch, TensorFlow, and JAX. Ini memberikan fleksibiliti untuk menggunakan rangka kerja yang berbeza pada setiap peringkat kehidupan model; latih model dalam tiga baris kod dalam satu rangka kerja, dan muatkannya untuk inferens dalam rangka kerja yang lain. Model juga boleh dieksport ke format seperti ONNX. + +Sertai komuniti yang semakin berkembang di [Hub](https://huggingface.co/models), [forum](https://discuss.huggingface.co/), atau [Discord](https://discord.com/invite/JfAtkvEtRb) hari ini! + +## Jika anda sedang mencari sokongan tersuai daripada pasukan Hugging Face + + +
+
+
+## Kandungan
+
+Dokumentasi disusun kepada lima bahagian:
+
+- **MULAKAN** menyediakan lawatan pantas ke perpustakaan dan arahan pemasangan untuk bangun dan berjalan.
+- **TUTORIAL** ialah tempat yang bagus untuk bermula jika anda seorang pemula. Bahagian ini akan membantu anda memperoleh kemahiran asas yang anda perlukan untuk mula menggunakan perpustakaan.
+- **PANDUAN CARA-CARA** menunjukkan kepada anda cara untuk mencapai matlamat tertentu, seperti memperhalusi model terlatih untuk pemodelan bahasa atau cara menulis dan berkongsi model tersuai.
+- **PANDUAN KONSEP** menawarkan lebih banyak perbincangan dan penjelasan tentang konsep dan idea asas di sebalik model, tugasan dan falsafah reka bentuk 🤗 Transformers.
+- **API** menerangkan semua kelas dan fungsi:
+
+ - **KELAS UTAMA** memperincikan kelas yang paling penting seperti konfigurasi, model, tokenizer dan saluran paip.
+ - **MODEL** memperincikan kelas dan fungsi yang berkaitan dengan setiap model yang dilaksanakan dalam perpustakaan.
+ - **PEMBANTU DALAMAN** memperincikan kelas utiliti dan fungsi yang digunakan secara dalaman.
+
+### Model yang disokong
+
+
+
+1. **[ALBERT](model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://huggingface.co/papers/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+1. **[ALIGN](model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://huggingface.co/papers/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
+1. **[AltCLIP](model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://huggingface.co/papers/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
+1. **[Audio Spectrogram Transformer](model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://huggingface.co/papers/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
+1. **[Autoformer](model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://huggingface.co/papers/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
+1. **[BART](model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://huggingface.co/papers/1910.13461) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
+1. **[BARThez](model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://huggingface.co/papers/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
+1. **[BARTpho](model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://huggingface.co/papers/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
+1. **[BEiT](model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://huggingface.co/papers/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
+1. **[BERT](model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://huggingface.co/papers/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
+1. **[BERT For Sequence Generation](model_doc/bert-generation)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+1. **[BERTweet](model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
+1. **[BigBird-Pegasus](model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://huggingface.co/papers/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+1. **[BigBird-RoBERTa](model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://huggingface.co/papers/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+1. **[BioGpt](model_doc/biogpt)** (from Microsoft Research AI4Science) released with the paper [BioGPT: generative pre-trained transformer for biomedical text generation and mining](https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac409/6713511?guestAccessKey=a66d9b5d-4f83-4017-bb52-405815c907b9) by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu.
+1. **[BiT](model_doc/bit)** (from Google AI) released with the paper [Big Transfer (BiT): General Visual Representation Learning](https://huggingface.co/papers/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
+1. **[Blenderbot](model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://huggingface.co/papers/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
+1. **[BlenderbotSmall](model_doc/blenderbot-small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://huggingface.co/papers/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
+1. **[BLIP](model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://huggingface.co/papers/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
+1. **[BLIP-2](model_doc/blip-2)** (from Salesforce) released with the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://huggingface.co/papers/2301.12597) by Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi.
+1. **[BLOOM](model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
+1. **[BORT](model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://huggingface.co/papers/2010.10499) by Adrian de Wynter and Daniel J. Perry.
+1. **[BridgeTower](model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://huggingface.co/papers/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
+1. **[Bros](model_doc/bros)** (from NAVER) released with the paper [BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents](https://huggingface.co/papers/2108.04539) by Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park.
+1. **[ByT5](model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://huggingface.co/papers/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
+1. **[CamemBERT](model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://huggingface.co/papers/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
+1. **[CANINE](model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://huggingface.co/papers/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
+1. **[Chinese-CLIP](model_doc/chinese_clip)** (from OFA-Sys) released with the paper [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://huggingface.co/papers/2211.01335) by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou.
+1. **[CLAP](model_doc/clap)** (from LAION-AI) released with the paper [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://huggingface.co/papers/2211.06687) by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
+1. **[CLIP](model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://huggingface.co/papers/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
+1. **[CLIPSeg](model_doc/clipseg)** (from University of Göttingen) released with the paper [Image Segmentation Using Text and Image Prompts](https://huggingface.co/papers/2112.10003) by Timo Lüddecke and Alexander Ecker.
+1. **[CodeGen](model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://huggingface.co/papers/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
+1. **[Conditional DETR](model_doc/conditional_detr)** (from Microsoft Research Asia) released with the paper [Conditional DETR for Fast Training Convergence](https://huggingface.co/papers/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.
+1. **[ConvBERT](model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://huggingface.co/papers/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
+1. **[ConvNeXT](model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://huggingface.co/papers/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
+1. **[ConvNeXTV2](model_doc/convnextv2)** (from Facebook AI) released with the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://huggingface.co/papers/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
+1. **[CPM](model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://huggingface.co/papers/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
+1. **[CPM-Ant](model_doc/cpmant)** (from OpenBMB) released by the [OpenBMB](https://www.openbmb.org/).
+1. **[CTRL](model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://huggingface.co/papers/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
+1. **[CvT](model_doc/cvt)** (from Microsoft) released with the paper [CvT: Introducing Convolutions to Vision Transformers](https://huggingface.co/papers/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang.
+1. **[Data2Vec](model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://huggingface.co/papers/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
+1. **[DeBERTa](model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://huggingface.co/papers/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[DeBERTa-v2](model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://huggingface.co/papers/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[Decision Transformer](model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://huggingface.co/papers/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
+1. **[Deformable DETR](model_doc/deformable_detr)** (from SenseTime Research) released with the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://huggingface.co/papers/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
+1. **[DeiT](model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://huggingface.co/papers/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
+1. **[DePlot](model_doc/deplot)** (from Google AI) released with the paper [DePlot: One-shot visual language reasoning by plot-to-table translation](https://huggingface.co/papers/2212.10505) by Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.
+1. **[DETA](model_doc/deta)** (from The University of Texas at Austin) released with the paper [NMS Strikes Back](https://huggingface.co/papers/2212.06137) by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl.
+1. **[DETR](model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://huggingface.co/papers/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
+1. **[DialoGPT](model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://huggingface.co/papers/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
+1. **[DiNAT](model_doc/dinat)** (from SHI Labs) released with the paper [Dilated Neighborhood Attention Transformer](https://huggingface.co/papers/2209.15001) by Ali Hassani and Humphrey Shi.
+1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://huggingface.co/papers/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers-research-projects/tree/main/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers-research-projects/tree/main/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers-research-projects/tree/main/distillation) and a German version of DistilBERT.
+1. **[DiT](model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://huggingface.co/papers/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
+1. **[Donut](model_doc/donut)** (from NAVER), released together with the paper [OCR-free Document Understanding Transformer](https://huggingface.co/papers/2111.15664) by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
+1. **[DPR](model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://huggingface.co/papers/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
+1. **[DPT](master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://huggingface.co/papers/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
+1. **[EfficientFormer](model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://huggingface.co/papers/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
+1. **[EfficientNet](model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://huggingface.co/papers/1905.11946) by Mingxing Tan, Quoc V. Le.
+1. **[ELECTRA](model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://huggingface.co/papers/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
+1. **[EncoderDecoder](model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+1. **[ERNIE](model_doc/ernie)** (from Baidu) released with the paper [ERNIE: Enhanced Representation through Knowledge Integration](https://huggingface.co/papers/1904.09223) by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu.
+1. **[ErnieM](model_doc/ernie_m)** (from Baidu) released with the paper [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://huggingface.co/papers/2012.15674) by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.
+1. **[ESM](model_doc/esm)** (from Meta AI) are transformer protein language models. **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2 and ESMFold** were released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
+1. **[FLAN-T5](model_doc/flan-t5)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
+1. **[FLAN-UL2](model_doc/flan-ul2)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-ul2-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
+1. **[FlauBERT](model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://huggingface.co/papers/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
+1. **[FLAVA](model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://huggingface.co/papers/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
+1. **[FNet](model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://huggingface.co/papers/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
+1. **[FocalNet](model_doc/focalnet)** (from Microsoft Research) released with the paper [Focal Modulation Networks](https://huggingface.co/papers/2203.11926) by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao.
+1. **[Funnel Transformer](model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://huggingface.co/papers/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
+1. **[GIT](model_doc/git)** (from Microsoft Research) released with the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://huggingface.co/papers/2205.14100) by Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang.
+1. **[GLPN](model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://huggingface.co/papers/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
+1. **[GPT](model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://openai.com/research/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
+1. **[GPT Neo](model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
+1. **[GPT NeoX](model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://huggingface.co/papers/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
+1. **[GPT NeoX Japanese](model_doc/gpt_neox_japanese)** (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori.
+1. **[GPT-2](model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://openai.com/research/better-language-models/) by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.
+1. **[GPT-J](model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
+1. **[GPT-Sw3](model_doc/gpt-sw3)** (from AI-Sweden) released with the paper [Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.376.pdf) by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman, Fredrik Carlsson, Magnus Sahlgren.
+1. **[GPTBigCode](model_doc/gpt_bigcode)** (from BigCode) released with the paper [SantaCoder: don't reach for the stars!](https://huggingface.co/papers/2301.03988) by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra.
+1. **[GPTSAN-japanese](model_doc/gptsan-japanese)** released in the repository [tanreinama/GPTSAN](https://github.com/tanreinama/GPTSAN/blob/main/report/model.md) by Toshiyuki Sakamoto(tanreinama).
+1. **[Graphormer](model_doc/graphormer)** (from Microsoft) released with the paper [Do Transformers Really Perform Bad for Graph Representation?](https://huggingface.co/papers/2106.05234) by Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu.
+1. **[GroupViT](model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://huggingface.co/papers/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
+1. **[Hubert](model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://huggingface.co/papers/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
+1. **[I-BERT](model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://huggingface.co/papers/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[ImageGPT](model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
+1. **[Informer](model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://huggingface.co/papers/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
+1. **[Jukebox](model_doc/jukebox)** (from OpenAI) released with the paper [Jukebox: A Generative Model for Music](https://huggingface.co/papers/2005.00341) by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever.
+1. **[LayoutLM](model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://huggingface.co/papers/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
+1. **[LayoutLMv2](model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://huggingface.co/papers/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
+1. **[LayoutLMv3](model_doc/layoutlmv3)** (from Microsoft Research Asia) released with the paper [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://huggingface.co/papers/2204.08387) by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
+1. **[LayoutXLM](model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://huggingface.co/papers/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
+1. **[LED](model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://huggingface.co/papers/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[LeViT](model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://huggingface.co/papers/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
+1. **[LiLT](model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://huggingface.co/papers/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
+1. **[LLaMA](model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://huggingface.co/papers/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
+1. **[Longformer](model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://huggingface.co/papers/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[LongT5](model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://huggingface.co/papers/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
+1. **[LUKE](model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://huggingface.co/papers/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
+1. **[LXMERT](model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://huggingface.co/papers/1908.07490) by Hao Tan and Mohit Bansal.
+1. **[M-CTC-T](model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://huggingface.co/papers/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
+1. **[M2M100](model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://huggingface.co/papers/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
+1. **[MarianMT](model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
+1. **[MarkupLM](model_doc/markuplm)** (from Microsoft Research Asia) released with the paper [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://huggingface.co/papers/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei.
+1. **[Mask2Former](model_doc/mask2former)** (from FAIR and UIUC) released with the paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://huggingface.co/papers/2112.01527) by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar.
+1. **[MaskFormer](model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://huggingface.co/papers/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
+1. **[MatCha](model_doc/matcha)** (from Google AI) released with the paper [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://huggingface.co/papers/2212.09662) by Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos.
+1. **[mBART](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://huggingface.co/papers/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
+1. **[mBART-50](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://huggingface.co/papers/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
+1. **[MEGA](model_doc/mega)** (from Meta/USC/CMU/SJTU) released with the paper [Mega: Moving Average Equipped Gated Attention](https://huggingface.co/papers/2209.10655) by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
+1. **[Megatron-BERT](model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://huggingface.co/papers/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[Megatron-GPT2](model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://huggingface.co/papers/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[MGP-STR](model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://huggingface.co/papers/2209.03592) by Peng Wang, Cheng Da, and Cong Yao.
+1. **[mLUKE](model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://huggingface.co/papers/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
+1. **[MobileBERT](model_doc/mobilebert)** (from CMU/Google Brain) released with the paper [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://huggingface.co/papers/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
+1. **[MobileNetV1](model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://huggingface.co/papers/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
+1. **[MobileNetV2](model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://huggingface.co/papers/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
+1. **[MobileViT](model_doc/mobilevit)** (from Apple) released with the paper [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://huggingface.co/papers/2110.02178) by Sachin Mehta and Mohammad Rastegari.
+1. **[MPNet](model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://huggingface.co/papers/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
+1. **[MT5](model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://huggingface.co/papers/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
+1. **[MVP](model_doc/mvp)** (from RUC AI Box) released with the paper [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://huggingface.co/papers/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
+1. **[NAT](model_doc/nat)** (from SHI Labs) released with the paper [Neighborhood Attention Transformer](https://huggingface.co/papers/2204.07143) by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.
+1. **[Nezha](model_doc/nezha)** (from Huawei Noah’s Ark Lab) released with the paper [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://huggingface.co/papers/1909.00204) by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
+1. **[NLLB](model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://huggingface.co/papers/2207.04672) by the NLLB team.
+1. **[NLLB-MOE](model_doc/nllb-moe)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://huggingface.co/papers/2207.04672) by the NLLB team.
+1. **[Nyströmformer](model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://huggingface.co/papers/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
+1. **[OneFormer](model_doc/oneformer)** (from SHI Labs) released with the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://huggingface.co/papers/2211.06220) by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
+1. **[OpenLlama](model_doc/open-llama)** (from [s-JoL](https://huggingface.co/s-JoL)) released on GitHub (now removed).
+1. **[OPT](master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://huggingface.co/papers/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
+1. **[OWL-ViT](model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://huggingface.co/papers/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
+1. **[Pegasus](model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://huggingface.co/papers/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
+1. **[PEGASUS-X](model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://huggingface.co/papers/2208.04347) by Jason Phang, Yao Zhao, and Peter J. Liu.
+1. **[Perceiver IO](model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://huggingface.co/papers/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
+1. **[PhoBERT](model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
+1. **[Pix2Struct](model_doc/pix2struct)** (from Google) released with the paper [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://huggingface.co/papers/2210.03347) by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.
+1. **[PLBart](model_doc/plbart)** (from UCLA NLP) released with the paper [Unified Pre-training for Program Understanding and Generation](https://huggingface.co/papers/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
+1. **[PoolFormer](model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://huggingface.co/papers/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
+1. **[ProphetNet](model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://huggingface.co/papers/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
+1. **[QDQBert](model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://huggingface.co/papers/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
+1. **[RAG](model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://huggingface.co/papers/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
+1. **[REALM](model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://huggingface.co/papers/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
+1. **[Reformer](model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://huggingface.co/papers/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
+1. **[RegNet](model_doc/regnet)** (from META Platforms) released with the paper [Designing Network Design Space](https://huggingface.co/papers/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
+1. **[RemBERT](model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://huggingface.co/papers/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
+1. **[ResNet](model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://huggingface.co/papers/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
+1. **[RoBERTa](model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://huggingface.co/papers/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
+1. **[RoBERTa-PreLayerNorm](model_doc/roberta-prelayernorm)** (from Facebook) released with the paper [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://huggingface.co/papers/1904.01038) by Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli.
+1. **[RoCBert](model_doc/roc_bert)** (from WeChatAI) released with the paper [RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining](https://aclanthology.org/2022.acl-long.65.pdf) by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
+1. **[RoFormer](model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://huggingface.co/papers/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[RWKV](model_doc/rwkv)** (from Bo Peng), released on [this repo](https://github.com/BlinkDL/RWKV-LM) by Bo Peng.
+1. **[SegFormer](model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://huggingface.co/papers/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
+1. **[Segment Anything](model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://huggingface.co/papers/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
+1. **[SEW](model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://huggingface.co/papers/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SEW-D](model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://huggingface.co/papers/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SpeechT5](model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://huggingface.co/papers/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
+1. **[SpeechToTextTransformer](model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://huggingface.co/papers/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
+1. **[SpeechToTextTransformer2](model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://huggingface.co/papers/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
+1. **[Splinter](model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://huggingface.co/papers/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
+1. **[SqueezeBERT](model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://huggingface.co/papers/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
+1. **[SwiftFormer](model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://huggingface.co/papers/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
+1. **[Swin Transformer](model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://huggingface.co/papers/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
+1. **[Swin Transformer V2](model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://huggingface.co/papers/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
+1. **[Swin2SR](model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://huggingface.co/papers/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
+1. **[SwitchTransformers](model_doc/switch_transformers)** (from Google) released with the paper [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://huggingface.co/papers/2101.03961) by William Fedus, Barret Zoph, Noam Shazeer.
+1. **[T5](model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://huggingface.co/papers/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
+1. **[T5v1.1](model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
+1. **[Table Transformer](model_doc/table-transformer)** (from Microsoft Research) released with the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://huggingface.co/papers/2110.00061) by Brandon Smock, Rohith Pesala, Robin Abraham.
+1. **[TAPAS](model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://huggingface.co/papers/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
+1. **[TAPEX](model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://huggingface.co/papers/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
+1. **[Time Series Transformer](model_doc/time_series_transformer)** (from HuggingFace).
+1. **[TimeSformer](model_doc/timesformer)** (from Facebook) released with the paper [Is Space-Time Attention All You Need for Video Understanding?](https://huggingface.co/papers/2102.05095) by Gedas Bertasius, Heng Wang, Lorenzo Torresani.
+1. **[Trajectory Transformer](model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://huggingface.co/papers/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
+1. **[Transformer-XL](model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://huggingface.co/papers/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
+1. **[TrOCR](model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://huggingface.co/papers/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
+1. **[TVLT](model_doc/tvlt)** (from UNC Chapel Hill) released with the paper [TVLT: Textless Vision-Language Transformer](https://huggingface.co/papers/2209.14156) by Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal.
+1. **[TVP](model_doc/tvp)** (from Intel) released with the paper [Text-Visual Prompting for Efficient 2D Temporal Video Grounding](https://huggingface.co/papers/2303.04995) by Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding.
+1. **[UL2](model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://huggingface.co/papers/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
+1. **[UniSpeech](model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://huggingface.co/papers/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
+1. **[UniSpeechSat](model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://huggingface.co/papers/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
+1. **[UPerNet](model_doc/upernet)** (from Peking University) released with the paper [Unified Perceptual Parsing for Scene Understanding](https://huggingface.co/papers/1807.10221) by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun.
+1. **[VAN](model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://huggingface.co/papers/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
+1. **[VideoMAE](model_doc/videomae)** (from Multimedia Computing Group, Nanjing University) released with the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://huggingface.co/papers/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
+1. **[ViLT](model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://huggingface.co/papers/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
+1. **[Vision Transformer (ViT)](model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://huggingface.co/papers/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
+1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://huggingface.co/papers/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
+1. **[ViT Hybrid](model_doc/vit_hybrid)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://huggingface.co/papers/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
+1. **[ViTMAE](model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://huggingface.co/papers/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
+1. **[ViTMSN](model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://huggingface.co/papers/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
+1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://huggingface.co/papers/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
+1. **[Wav2Vec2-Conformer](model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://huggingface.co/papers/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
+1. **[Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://huggingface.co/papers/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
+1. **[WavLM](model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://huggingface.co/papers/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
+1. **[Whisper](model_doc/whisper)** (from OpenAI) released with the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
+1. **[X-CLIP](model_doc/xclip)** (from Microsoft Research) released with the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://huggingface.co/papers/2208.02816) by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
+1. **[X-MOD](model_doc/xmod)** (from Meta AI) released with the paper [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](http://dx.doi.org/10.18653/v1/2022.naacl-main.255) by Jonas Pfeiffer, Naman Goyal, Xi Lin, Xian Li, James Cross, Sebastian Riedel, Mikel Artetxe.
+1. **[XGLM](model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://huggingface.co/papers/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
+1. **[XLM](model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://huggingface.co/papers/1901.07291) by Guillaume Lample and Alexis Conneau.
+1. **[XLM-ProphetNet](model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://huggingface.co/papers/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
+1. **[XLM-RoBERTa](model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://huggingface.co/papers/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
+1. **[XLM-RoBERTa-XL](model_doc/xlm-roberta-xl)** (from Facebook AI), released together with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://huggingface.co/papers/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
+1. **[XLM-V](model_doc/xlm-v)** (from Meta AI) released with the paper [XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://huggingface.co/papers/2301.10472) by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa.
+1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://huggingface.co/papers/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+1. **[XLS-R](model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://huggingface.co/papers/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
+1. **[XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://huggingface.co/papers/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
+1. **[YOLOS](model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://huggingface.co/papers/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
+1. **[YOSO](model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://huggingface.co/papers/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
+
+
+### Rangka kerja yang disokong
+
+Jadual di bawah mewakili sokongan semasa dalam perpustakaan untuk setiap model tersebut, sama ada model tersebut mempunyai Python
+tokenizer (dipanggil ""lambat""). Tokenizer ""pantas"" yang disokong oleh perpustakaan Tokenizers 🤗, sama ada mereka mempunyai sokongan dalam Jax (melalui
+Flax), PyTorch, dan/atau TensorFlow.
+
+
+
+| Model | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
+|:-----------------------------:|:--------------:|:--------------:|:---------------:|:------------------:|:------------:|
+| ALBERT | ✅ | ✅ | ✅ | ✅ | ✅ |
+| ALIGN | ❌ | ❌ | ✅ | ❌ | ❌ |
+| AltCLIP | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Audio Spectrogram Transformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Autoformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| BART | ✅ | ✅ | ✅ | ✅ | ✅ |
+| BEiT | ❌ | ❌ | ✅ | ❌ | ✅ |
+| BERT | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Bert Generation | ✅ | ❌ | ✅ | ❌ | ❌ |
+| BigBird | ✅ | ✅ | ✅ | ❌ | ✅ |
+| BigBird-Pegasus | ❌ | ❌ | ✅ | ❌ | ❌ |
+| BioGpt | ✅ | ❌ | ✅ | ❌ | ❌ |
+| BiT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Blenderbot | ✅ | ✅ | ✅ | ✅ | ✅ |
+| BlenderbotSmall | ✅ | ✅ | ✅ | ✅ | ✅ |
+| BLIP | ❌ | ❌ | ✅ | ✅ | ❌ |
+| BLIP-2 | ❌ | ❌ | ✅ | ❌ | ❌ |
+| BLOOM | ❌ | ✅ | ✅ | ❌ | ❌ |
+| BridgeTower | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Bros | ✅ | ✅ | ✅ | ❌ | ❌ |
+| CamemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| CANINE | ✅ | ❌ | ✅ | ❌ | ❌ |
+| Chinese-CLIP | ❌ | ❌ | ✅ | ❌ | ❌ |
+| CLAP | ❌ | ❌ | ✅ | ❌ | ❌ |
+| CLIP | ✅ | ✅ | ✅ | ✅ | ✅ |
+| CLIPSeg | ❌ | ❌ | ✅ | ❌ | ❌ |
+| CodeGen | ✅ | ✅ | ✅ | ❌ | ❌ |
+| Conditional DETR | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ConvBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| ConvNeXT | ❌ | ❌ | ✅ | ✅ | ❌ |
+| ConvNeXTV2 | ❌ | ❌ | ✅ | ❌ | ❌ |
+| CPM-Ant | ✅ | ❌ | ✅ | ❌ | ❌ |
+| CTRL | ✅ | ❌ | ✅ | ✅ | ❌ |
+| CvT | ❌ | ❌ | ✅ | ✅ | ❌ |
+| Data2VecAudio | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Data2VecText | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Data2VecVision | ❌ | ❌ | ✅ | ✅ | ❌ |
+| DeBERTa | ✅ | ✅ | ✅ | ✅ | ❌ |
+| DeBERTa-v2 | ✅ | ✅ | ✅ | ✅ | ❌ |
+| Decision Transformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Deformable DETR | ❌ | ❌ | ✅ | ❌ | ❌ |
+| DeiT | ❌ | ❌ | ✅ | ✅ | ❌ |
+| DETA | ❌ | ❌ | ✅ | ❌ | ❌ |
+| DETR | ❌ | ❌ | ✅ | ❌ | ❌ |
+| DiNAT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| DistilBERT | ✅ | ✅ | ✅ | ✅ | ✅ |
+| DonutSwin | ❌ | ❌ | ✅ | ❌ | ❌ |
+| DPR | ✅ | ✅ | ✅ | ✅ | ❌ |
+| DPT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| EfficientFormer | ❌ | ❌ | ✅ | ✅ | ❌ |
+| EfficientNet | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ELECTRA | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅ |
+| ERNIE | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ErnieM | ✅ | ❌ | ✅ | ❌ | ❌ |
+| ESM | ✅ | ❌ | ✅ | ✅ | ❌ |
+| FairSeq Machine-Translation | ✅ | ❌ | ✅ | ❌ | ❌ |
+| FlauBERT | ✅ | ❌ | ✅ | ✅ | ❌ |
+| FLAVA | ❌ | ❌ | ✅ | ❌ | ❌ |
+| FNet | ✅ | ✅ | ✅ | ❌ | ❌ |
+| FocalNet | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Funnel Transformer | ✅ | ✅ | ✅ | ✅ | ❌ |
+| GIT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| GLPN | ❌ | ❌ | ✅ | ❌ | ❌ |
+| GPT Neo | ❌ | ❌ | ✅ | ❌ | ✅ |
+| GPT NeoX | ❌ | ✅ | ✅ | ❌ | ❌ |
+| GPT NeoX Japanese | ✅ | ❌ | ✅ | ❌ | ❌ |
+| GPT-J | ❌ | ❌ | ✅ | ✅ | ✅ |
+| GPT-Sw3 | ✅ | ✅ | ✅ | ✅ | ✅ |
+| GPTBigCode | ❌ | ❌ | ✅ | ❌ | ❌ |
+| GPTSAN-japanese | ✅ | ❌ | ✅ | ❌ | ❌ |
+| Graphormer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| GroupViT | ❌ | ❌ | ✅ | ✅ | ❌ |
+| Hubert | ❌ | ❌ | ✅ | ✅ | ❌ |
+| I-BERT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ImageGPT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Informer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Jukebox | ✅ | ❌ | ✅ | ❌ | ❌ |
+| LayoutLM | ✅ | ✅ | ✅ | ✅ | ❌ |
+| LayoutLMv2 | ✅ | ✅ | ✅ | ❌ | ❌ |
+| LayoutLMv3 | ✅ | ✅ | ✅ | ✅ | ❌ |
+| LED | ✅ | ✅ | ✅ | ✅ | ❌ |
+| LeViT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| LiLT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| LLaMA | ✅ | ✅ | ✅ | ❌ | ❌ |
+| Longformer | ✅ | ✅ | ✅ | ✅ | ❌ |
+| LongT5 | ❌ | ❌ | ✅ | ❌ | ✅ |
+| LUKE | ✅ | ❌ | ✅ | ❌ | ❌ |
+| LXMERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| M-CTC-T | ❌ | ❌ | ✅ | ❌ | ❌ |
+| M2M100 | ✅ | ❌ | ✅ | ❌ | ❌ |
+| Marian | ✅ | ❌ | ✅ | ✅ | ✅ |
+| MarkupLM | ✅ | ✅ | ✅ | ❌ | ❌ |
+| Mask2Former | ❌ | ❌ | ✅ | ❌ | ❌ |
+| MaskFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| MaskFormerSwin | ❌ | ❌ | ❌ | ❌ | ❌ |
+| mBART | ✅ | ✅ | ✅ | ✅ | ✅ |
+| MEGA | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Megatron-BERT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| MGP-STR | ✅ | ❌ | ✅ | ❌ | ❌ |
+| MobileBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| MobileNetV1 | ❌ | ❌ | ✅ | ❌ | ❌ |
+| MobileNetV2 | ❌ | ❌ | ✅ | ❌ | ❌ |
+| MobileViT | ❌ | ❌ | ✅ | ✅ | ❌ |
+| MPNet | ✅ | ✅ | ✅ | ✅ | ❌ |
+| MT5 | ✅ | ✅ | ✅ | ✅ | ✅ |
+| MVP | ✅ | ✅ | ✅ | ❌ | ❌ |
+| NAT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Nezha | ❌ | ❌ | ✅ | ❌ | ❌ |
+| NLLB-MOE | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Nyströmformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| OneFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| OpenAI GPT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| OpenAI GPT-2 | ✅ | ✅ | ✅ | ✅ | ✅ |
+| OpenLlama | ❌ | ❌ | ✅ | ❌ | ❌ |
+| OPT | ❌ | ❌ | ✅ | ✅ | ✅ |
+| OWL-ViT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Pegasus | ✅ | ✅ | ✅ | ✅ | ✅ |
+| PEGASUS-X | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Perceiver | ✅ | ❌ | ✅ | ❌ | ❌ |
+| Pix2Struct | ❌ | ❌ | ✅ | ❌ | ❌ |
+| PLBart | ✅ | ❌ | ✅ | ❌ | ❌ |
+| PoolFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
+| QDQBert | ❌ | ❌ | ✅ | ❌ | ❌ |
+| RAG | ✅ | ❌ | ✅ | ✅ | ❌ |
+| REALM | ✅ | ✅ | ✅ | ❌ | ❌ |
+| Reformer | ✅ | ✅ | ✅ | ❌ | ❌ |
+| RegNet | ❌ | ❌ | ✅ | ✅ | ✅ |
+| RemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| ResNet | ❌ | ❌ | ✅ | ✅ | ✅ |
+| RetriBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
+| RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ |
+| RoBERTa-PreLayerNorm | ❌ | ❌ | ✅ | ✅ | ✅ |
+| RoCBert | ✅ | ❌ | ✅ | ❌ | ❌ |
+| RoFormer | ✅ | ✅ | ✅ | ✅ | ✅ |
+| RWKV | ❌ | ❌ | ✅ | ❌ | ❌ |
+| SAM | ❌ | ❌ | ✅ | ✅ | ❌ |
+| SegFormer | ❌ | ❌ | ✅ | ✅ | ❌ |
+| SEW | ❌ | ❌ | ✅ | ❌ | ❌ |
+| SEW-D | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Speech Encoder decoder | ❌ | ❌ | ✅ | ❌ | ✅ |
+| Speech2Text | ✅ | ❌ | ✅ | ✅ | ❌ |
+| Speech2Text2 | ✅ | ❌ | ❌ | ❌ | ❌ |
+| SpeechT5 | ✅ | ❌ | ✅ | ❌ | ❌ |
+| Splinter | ✅ | ✅ | ✅ | ❌ | ❌ |
+| SqueezeBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
+| SwiftFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Swin Transformer | ❌ | ❌ | ✅ | ✅ | ❌ |
+| Swin Transformer V2 | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Swin2SR | ❌ | ❌ | ✅ | ❌ | ❌ |
+| SwitchTransformers | ❌ | ❌ | ✅ | ❌ | ❌ |
+| T5 | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Table Transformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| TAPAS | ✅ | ❌ | ✅ | ✅ | ❌ |
+| Time Series Transformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| TimeSformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Trajectory Transformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Transformer-XL | ✅ | ❌ | ✅ | ✅ | ❌ |
+| TrOCR | ❌ | ❌ | ✅ | ❌ | ❌ |
+| TVLT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| TVP | ❌ | ❌ | ✅ | ❌ | ❌ |
+| UniSpeech | ❌ | ❌ | ✅ | ❌ | ❌ |
+| UniSpeechSat | ❌ | ❌ | ✅ | ❌ | ❌ |
+| UPerNet | ❌ | ❌ | ✅ | ❌ | ❌ |
+| VAN | ❌ | ❌ | ✅ | ❌ | ❌ |
+| VideoMAE | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ViLT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Vision Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅ |
+| VisionTextDualEncoder | ❌ | ❌ | ✅ | ✅ | ✅ |
+| VisualBERT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ViT | ❌ | ❌ | ✅ | ✅ | ✅ |
+| ViT Hybrid | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ViTMAE | ❌ | ❌ | ✅ | ✅ | ❌ |
+| ViTMSN | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Wav2Vec2 | ✅ | ❌ | ✅ | ✅ | ✅ |
+| Wav2Vec2-Conformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| WavLM | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Whisper | ✅ | ✅ | ✅ | ✅ | ✅ |
+| X-CLIP | ❌ | ❌ | ✅ | ❌ | ❌ |
+| X-MOD | ❌ | ❌ | ✅ | ❌ | ❌ |
+| XGLM | ✅ | ✅ | ✅ | ✅ | ✅ |
+| XLM | ✅ | ❌ | ✅ | ✅ | ❌ |
+| XLM-ProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
+| XLM-RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ |
+| XLM-RoBERTa-XL | ❌ | ❌ | ✅ | ❌ | ❌ |
+| XLNet | ✅ | ✅ | ✅ | ✅ | ❌ |
+| YOLOS | ❌ | ❌ | ✅ | ❌ | ❌ |
+| YOSO | ❌ | ❌ | ✅ | ❌ | ❌ |
+
+
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/_config.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..f49e4e4731965a504b8da443c2cd979638cd22bb
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/_config.py
@@ -0,0 +1,14 @@
+# docstyle-ignore
+INSTALL_CONTENT = """
+# Transformers installation
+! pip install transformers datasets evaluate accelerate
+# To install from source instead of the last release, comment the command above and uncomment the following one.
+# ! pip install git+https://github.com/huggingface/transformers.git
+"""
+
+notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}]
+black_avoid_patterns = {
+ "{processor_class}": "FakeProcessorClass",
+ "{model_class}": "FakeModelClass",
+ "{object_class}": "FakeObjectClass",
+}
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/_toctree.yml b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/_toctree.yml
new file mode 100644
index 0000000000000000000000000000000000000000..d042168f7b9b693d92c0f85e2142ca1ec43e1236
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/_toctree.yml
@@ -0,0 +1,38 @@
+- sections:
+ - local: index
+ title: 🤗 Transformers
+ - local: quicktour
+ title: Tour rápido
+ - local: installation
+ title: Instalação
+ title: Início
+- sections:
+ - local: pipeline_tutorial
+ title: Pipelines para inferência
+ - local: training
+ title: Fine-tuning de um modelo pré-treinado
+ - local: accelerate
+ title: Treinamento distribuído com 🤗 Accelerate
+ title: Tutoriais
+- sections:
+ - local: fast_tokenizers
+ title: Usando os Tokenizers do 🤗 Tokenizers
+ - local: create_a_model
+ title: Criando uma arquitetura customizada
+ - local: custom_models
+ title: Compartilhando modelos customizados
+ - local: run_scripts
+ title: Treinamento a partir de um script
+ - local: converting_tensorflow_models
+ title: Convertendo checkpoints do TensorFlow para Pytorch
+ - local: serialization
+ title: Exportando modelos para ONNX
+ - sections:
+ - local: tasks/sequence_classification
+ title: Classificação de texto
+ - local: tasks/token_classification
+ title: Classificação de tokens
+ title: Fine-tuning para tarefas específicas
+ - local: multilingual
+ title: Modelos multilinguísticos para inferência
+ title: Guias práticos
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/accelerate.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/accelerate.md
new file mode 100644
index 0000000000000000000000000000000000000000..a4e346a2b4873ff475ff9c3e34ff75e276fb58a7
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/accelerate.md
@@ -0,0 +1,145 @@
+
+
+# Treinamento distribuído com o 🤗 Accelerate
+
+O paralelismo surgiu como uma estratégia para treinar modelos grandes em hardware limitado e aumentar a velocidade
+de treinamento em várias órdens de magnitude. Na Hugging Face criamos a biblioteca [🤗 Accelerate](https://huggingface.co/docs/accelerate)
+para ajudar os usuários a treinar modelos 🤗 Transformers com qualquer configuração distribuída, seja em uma máquina
+com múltiplos GPUs ou em múltiplos GPUs distribuidos entre muitas máquinas. Neste tutorial, você irá aprender como
+personalizar seu laço de treinamento de PyTorch para poder treinar em ambientes distribuídos.
+
+## Configuração
+
+De início, instale o 🤗 Accelerate:
+
+```bash
+pip install accelerate
+```
+
+Logo, devemos importar e criar um objeto [`Accelerator`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator).
+O `Accelerator` detectará automáticamente a configuração distribuída disponível e inicializará todos os
+componentes necessários para o treinamento. Não há necessidade portanto de especificar o dispositivo onde deve colocar seu modelo.
+
+```py
+>>> from accelerate import Accelerator
+
+>>> accelerator = Accelerator()
+```
+
+## Preparando a aceleração
+
+Passe todos os objetos relevantes ao treinamento para o método [`prepare`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.prepare).
+Isto inclui os DataLoaders de treino e evaluação, um modelo e um otimizador:
+
+```py
+>>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
+... train_dataloader, eval_dataloader, model, optimizer
+... )
+```
+
+## Backward
+
+Por último, substitua o `loss.backward()` padrão em seu laço de treinamento com o método [`backward`](https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.backward) do 🤗 Accelerate:
+
+```py
+>>> for epoch in range(num_epochs):
+... for batch in train_dataloader:
+... outputs = model(**batch)
+... loss = outputs.loss
+... accelerator.backward(loss)
+
+... optimizer.step()
+... lr_scheduler.step()
+... optimizer.zero_grad()
+... progress_bar.update(1)
+```
+
+Como se poder ver no seguinte código, só precisará adicionar quatro linhas de código ao seu laço de treinamento
+para habilitar o treinamento distribuído!
+
+```diff
++ from accelerate import Accelerator
+ from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
+
++ accelerator = Accelerator()
+
+ model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
+ optimizer = AdamW(model.parameters(), lr=3e-5)
+
+- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+- model.to(device)
+
++ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
++ train_dataloader, eval_dataloader, model, optimizer
++ )
+
+ num_epochs = 3
+ num_training_steps = num_epochs * len(train_dataloader)
+ lr_scheduler = get_scheduler(
+ "linear",
+ optimizer=optimizer,
+ num_warmup_steps=0,
+ num_training_steps=num_training_steps
+ )
+
+ progress_bar = tqdm(range(num_training_steps))
+
+ model.train()
+ for epoch in range(num_epochs):
+ for batch in train_dataloader:
+- batch = {k: v.to(device) for k, v in batch.items()}
+ outputs = model(**batch)
+ loss = outputs.loss
+- loss.backward()
++ accelerator.backward(loss)
+
+ optimizer.step()
+ lr_scheduler.step()
+ optimizer.zero_grad()
+ progress_bar.update(1)
+```
+
+## Treinamento
+
+Quando tiver adicionado as linhas de código relevantes, inicie o treinamento por um script ou notebook como o Colab.
+
+### Treinamento em um Script
+
+Se estiver rodando seu treinamento em um Script, execute o seguinte comando para criar e guardar um arquivo de configuração:
+
+```bash
+accelerate config
+```
+
+Comece o treinamento com:
+
+```bash
+accelerate launch train.py
+```
+
+### Treinamento em um Notebook
+
+O 🤗 Accelerate pode rodar em um notebook, por exemplo, se estiver planejando usar as TPUs do Google Colab.
+Encapsule o código responsável pelo treinamento de uma função e passe-o ao `notebook_launcher`:
+
+```py
+>>> from accelerate import notebook_launcher
+
+>>> notebook_launcher(training_function)
+```
+
+Para obter mais informações sobre o 🤗 Accelerate e suas numerosas funções, consulte a [documentación](https://huggingface.co/docs/accelerate/index).
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/converting_tensorflow_models.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/converting_tensorflow_models.md
new file mode 100644
index 0000000000000000000000000000000000000000..446acd62ea8ffd2b422f9f9a84b5091c4788156d
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/converting_tensorflow_models.md
@@ -0,0 +1,152 @@
+
+
+# Convertendo checkpoints do TensorFlow para Pytorch
+
+Uma interface de linha de comando é fornecida para converter os checkpoints originais Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM em modelos
+que podem ser carregados usando os métodos `from_pretrained` da biblioteca.
+
+
+
+
+## Conteúdo
+
+A documentação é dividida em cinco partes:
+ - **INÍCIO** contém um tour rápido de instalação e instruções para te dar um empurrão inicial com os 🤗 Transformers.
+ - **TUTORIAIS** são perfeitos para começar a aprender sobre a nossa biblioteca. Essa seção irá te ajudar a desenvolver
+ habilidades básicas necessárias para usar o 🤗 Transformers.
+ - **GUIAS PRÁTICOS** irão te mostrar como alcançar um certo objetivo, como o fine-tuning de um modelo pré-treinado
+ para modelamento de idioma, ou como criar um cabeçalho personalizado para um modelo.
+ - **GUIAS CONCEITUAIS** te darão mais discussões e explicações dos conceitos fundamentais e idéias por trás dos modelos,
+ tarefas e da filosofia de design por trás do 🤗 Transformers.
+ - **API** descreve o funcionamento de cada classe e função, agrupada em:
+
+ - **CLASSES PRINCIPAIS** para as classes que expõe as APIs importantes da biblioteca.
+ - **MODELOS** para as classes e funções relacionadas à cada modelo implementado na biblioteca.
+ - **AUXILIARES INTERNOS** para as classes e funções usadas internamente.
+
+Atualmente a biblioteca contém implementações do PyTorch, TensorFlow e JAX, pesos para modelos pré-treinados e scripts de uso e conversão de utilidades para os seguintes modelos:
+
+### Modelos atuais
+
+
+
+1. **[ALBERT](model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://huggingface.co/papers/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
+1. **[BART](model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://huggingface.co/papers/1910.13461) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
+1. **[BARThez](model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://huggingface.co/papers/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
+1. **[BARTpho](model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://huggingface.co/papers/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
+1. **[BEiT](model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://huggingface.co/papers/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
+1. **[BERT](model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://huggingface.co/papers/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
+1. **[BERTweet](model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
+1. **[BERT For Sequence Generation](model_doc/bert-generation)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+1. **[BigBird-RoBERTa](model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://huggingface.co/papers/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+1. **[BigBird-Pegasus](model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://huggingface.co/papers/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
+1. **[Blenderbot](model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://huggingface.co/papers/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
+1. **[BlenderbotSmall](model_doc/blenderbot-small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://huggingface.co/papers/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
+1. **[BORT](model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://huggingface.co/papers/2010.10499) by Adrian de Wynter and Daniel J. Perry.
+1. **[ByT5](model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://huggingface.co/papers/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
+1. **[CamemBERT](model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://huggingface.co/papers/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
+1. **[CANINE](model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://huggingface.co/papers/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
+1. **[ConvNeXT](model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://huggingface.co/papers/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
+1. **[ConvNeXTV2](model_doc/convnextv2)** (from Facebook AI) released with the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://huggingface.co/papers/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
+1. **[CLIP](model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://huggingface.co/papers/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
+1. **[ConvBERT](model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://huggingface.co/papers/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
+1. **[CPM](model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://huggingface.co/papers/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
+1. **[CTRL](model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://huggingface.co/papers/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
+1. **[Data2Vec](model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://huggingface.co/papers/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
+1. **[DeBERTa](model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://huggingface.co/papers/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[DeBERTa-v2](model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://huggingface.co/papers/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
+1. **[Decision Transformer](model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://huggingface.co/papers/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
+1. **[DiT](model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://huggingface.co/papers/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
+1. **[DeiT](model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://huggingface.co/papers/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
+1. **[DETR](model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://huggingface.co/papers/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
+1. **[DialoGPT](model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://huggingface.co/papers/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
+1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://huggingface.co/papers/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers-research-projects/tree/main/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers-research-projects/tree/main/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers-research-projects/tree/main/distillation) and a German version of DistilBERT.
+1. **[DPR](model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://huggingface.co/papers/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
+1. **[DPT](master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://huggingface.co/papers/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
+1. **[EfficientNet](model_doc/efficientnet)** (from Google Research) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://huggingface.co/papers/1905.11946) by Mingxing Tan and Quoc V. Le.
+1. **[EncoderDecoder](model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
+1. **[ELECTRA](model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://huggingface.co/papers/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
+1. **[FlauBERT](model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://huggingface.co/papers/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
+1. **[FNet](model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://huggingface.co/papers/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
+1. **[Funnel Transformer](model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://huggingface.co/papers/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
+1. **[GLPN](model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://huggingface.co/papers/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
+1. **[GPT](model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://openai.com/research/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
+1. **[GPT-2](model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://openai.com/research/better-language-models/) by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.
+1. **[GPT-J](model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
+1. **[GPT Neo](model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
+1. **[GPTSAN-japanese](model_doc/gptsan-japanese)** released in the repository [tanreinama/GPTSAN](https://github.com/tanreinama/GPTSAN/blob/main/report/model.md) by Toshiyuki Sakamoto(tanreinama).
+1. **[Hubert](model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://huggingface.co/papers/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
+1. **[I-BERT](model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://huggingface.co/papers/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
+1. **[ImageGPT](model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
+1. **[LayoutLM](model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://huggingface.co/papers/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
+1. **[LayoutLMv2](model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://huggingface.co/papers/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
+1. **[LayoutXLM](model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://huggingface.co/papers/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
+1. **[LED](model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://huggingface.co/papers/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[Longformer](model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://huggingface.co/papers/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
+1. **[LUKE](model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://huggingface.co/papers/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
+1. **[mLUKE](model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://huggingface.co/papers/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
+1. **[LXMERT](model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://huggingface.co/papers/1908.07490) by Hao Tan and Mohit Bansal.
+1. **[M2M100](model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://huggingface.co/papers/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
+1. **[MarianMT](model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
+1. **[Mask2Former](model_doc/mask2former)** (from FAIR and UIUC) released with the paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://huggingface.co/papers/2112.01527) by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar.
+1. **[MaskFormer](model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://huggingface.co/papers/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
+1. **[MBart](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://huggingface.co/papers/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
+1. **[MBart-50](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://huggingface.co/papers/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
+1. **[Megatron-BERT](model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://huggingface.co/papers/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[Megatron-GPT2](model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://huggingface.co/papers/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
+1. **[MPNet](model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://huggingface.co/papers/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
+1. **[MT5](model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://huggingface.co/papers/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
+1. **[Nyströmformer](model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://huggingface.co/papers/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
+1. **[OneFormer](model_doc/oneformer)** (from SHI Labs) released with the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://huggingface.co/papers/2211.06220) by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
+1. **[Pegasus](model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://huggingface.co/papers/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
+1. **[Perceiver IO](model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://huggingface.co/papers/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
+1. **[PhoBERT](model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
+1. **[PLBart](model_doc/plbart)** (from UCLA NLP) released with the paper [Unified Pre-training for Program Understanding and Generation](https://huggingface.co/papers/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
+1. **[PoolFormer](model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://huggingface.co/papers/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
+1. **[ProphetNet](model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://huggingface.co/papers/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
+1. **[QDQBert](model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://huggingface.co/papers/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
+1. **[REALM](model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://huggingface.co/papers/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
+1. **[Reformer](model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://huggingface.co/papers/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
+1. **[RemBERT](model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://huggingface.co/papers/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
+1. **[RegNet](model_doc/regnet)** (from META Platforms) released with the paper [Designing Network Design Space](https://huggingface.co/papers/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
+1. **[ResNet](model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://huggingface.co/papers/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
+1. **[RoBERTa](model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://huggingface.co/papers/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
+1. **[RoFormer](model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://huggingface.co/papers/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+1. **[SegFormer](model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://huggingface.co/papers/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
+1. **[SEW](model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://huggingface.co/papers/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SEW-D](model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://huggingface.co/papers/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
+1. **[SpeechToTextTransformer](model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://huggingface.co/papers/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
+1. **[SpeechToTextTransformer2](model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://huggingface.co/papers/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
+1. **[Splinter](model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://huggingface.co/papers/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
+1. **[SqueezeBert](model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://huggingface.co/papers/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
+1. **[Swin Transformer](model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://huggingface.co/papers/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
+1. **[T5](model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://huggingface.co/papers/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
+1. **[T5v1.1](model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
+1. **[TAPAS](model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://huggingface.co/papers/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
+1. **[TAPEX](model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://huggingface.co/papers/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
+1. **[Transformer-XL](model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://huggingface.co/papers/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
+1. **[TrOCR](model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://huggingface.co/papers/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
+1. **[UniSpeech](model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://huggingface.co/papers/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
+1. **[UniSpeechSat](model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://huggingface.co/papers/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
+1. **[VAN](model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://huggingface.co/papers/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
+1. **[ViLT](model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://huggingface.co/papers/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
+1. **[Vision Transformer (ViT)](model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://huggingface.co/papers/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
+1. **[ViTMAE](model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://huggingface.co/papers/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
+1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://huggingface.co/papers/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
+1. **[WavLM](model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://huggingface.co/papers/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
+1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://huggingface.co/papers/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
+1. **[Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://huggingface.co/papers/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
+1. **[XGLM](model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://huggingface.co/papers/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
+1. **[XLM](model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://huggingface.co/papers/1901.07291) by Guillaume Lample and Alexis Conneau.
+1. **[XLM-ProphetNet](model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://huggingface.co/papers/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
+1. **[XLM-RoBERTa](model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://huggingface.co/papers/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
+1. **[XLM-RoBERTa-XL](model_doc/xlm-roberta-xl)** (from Facebook AI), released together with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://huggingface.co/papers/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
+1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://huggingface.co/papers/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+1. **[XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://huggingface.co/papers/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
+1. **[XLS-R](model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://huggingface.co/papers/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
+1. **[YOSO](model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://huggingface.co/papers/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
+
+
+### Frameworks aceitos
+
+A tabela abaixo representa a lista de suporte na biblioteca para cada um dos seguintes modelos, caso tenham um tokenizer
+do Python (chamado de "slow"), ou um tokenizer construído em cima da biblioteca 🤗 Tokenizers (chamado de "fast"). Além
+disso, são diferenciados pelo suporte em diferentes frameworks: JAX (por meio do Flax); PyTorch; e/ou Tensorflow.
+
+
+
+| Model | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
+|:---------------------------:|:--------------:|:--------------:|:---------------:|:------------------:|:------------:|
+| ALBERT | ✅ | ✅ | ✅ | ✅ | ✅ |
+| BART | ✅ | ✅ | ✅ | ✅ | ✅ |
+| BEiT | ❌ | ❌ | ✅ | ❌ | ✅ |
+| BERT | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Bert Generation | ✅ | ❌ | ✅ | ❌ | ❌ |
+| BigBird | ✅ | ✅ | ✅ | ❌ | ✅ |
+| BigBirdPegasus | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Blenderbot | ✅ | ✅ | ✅ | ✅ | ✅ |
+| BlenderbotSmall | ✅ | ✅ | ✅ | ✅ | ✅ |
+| CamemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| Canine | ✅ | ❌ | ✅ | ❌ | ❌ |
+| CLIP | ✅ | ✅ | ✅ | ✅ | ✅ |
+| ConvBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| ConvNext | ❌ | ❌ | ✅ | ✅ | ❌ |
+| CTRL | ✅ | ❌ | ✅ | ✅ | ❌ |
+| Data2VecAudio | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Data2VecText | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Data2VecVision | ❌ | ❌ | ✅ | ❌ | ❌ |
+| DeBERTa | ✅ | ✅ | ✅ | ✅ | ❌ |
+| DeBERTa-v2 | ✅ | ✅ | ✅ | ✅ | ❌ |
+| Decision Transformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| DeiT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| DETR | ❌ | ❌ | ✅ | ❌ | ❌ |
+| DistilBERT | ✅ | ✅ | ✅ | ✅ | ✅ |
+| DPR | ✅ | ✅ | ✅ | ✅ | ❌ |
+| DPT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ELECTRA | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅ |
+| FairSeq Machine-Translation | ✅ | ❌ | ✅ | ❌ | ❌ |
+| FlauBERT | ✅ | ❌ | ✅ | ✅ | ❌ |
+| FNet | ✅ | ✅ | ✅ | ❌ | ❌ |
+| Funnel Transformer | ✅ | ✅ | ✅ | ✅ | ❌ |
+| GLPN | ❌ | ❌ | ✅ | ❌ | ❌ |
+| GPT Neo | ❌ | ❌ | ✅ | ❌ | ✅ |
+| GPT-J | ❌ | ❌ | ✅ | ✅ | ✅ |
+| Hubert | ❌ | ❌ | ✅ | ✅ | ❌ |
+| I-BERT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ImageGPT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| LayoutLM | ✅ | ✅ | ✅ | ✅ | ❌ |
+| LayoutLMv2 | ✅ | ✅ | ✅ | ❌ | ❌ |
+| LED | ✅ | ✅ | ✅ | ✅ | ❌ |
+| Longformer | ✅ | ✅ | ✅ | ✅ | ❌ |
+| LUKE | ✅ | ❌ | ✅ | ❌ | ❌ |
+| LXMERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| M2M100 | ✅ | ❌ | ✅ | ❌ | ❌ |
+| Marian | ✅ | ❌ | ✅ | ✅ | ✅ |
+| MaskFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| mBART | ✅ | ✅ | ✅ | ✅ | ✅ |
+| MegatronBert | ❌ | ❌ | ✅ | ❌ | ❌ |
+| MobileBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| MPNet | ✅ | ✅ | ✅ | ✅ | ❌ |
+| mT5 | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Nystromformer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| OpenAI GPT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| OpenAI GPT-2 | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Pegasus | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Perceiver | ✅ | ❌ | ✅ | ❌ | ❌ |
+| PLBart | ✅ | ❌ | ✅ | ❌ | ❌ |
+| PoolFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
+| QDQBert | ❌ | ❌ | ✅ | ❌ | ❌ |
+| RAG | ✅ | ❌ | ✅ | ✅ | ❌ |
+| Realm | ✅ | ✅ | ✅ | ❌ | ❌ |
+| Reformer | ✅ | ✅ | ✅ | ❌ | ❌ |
+| RegNet | ❌ | ❌ | ✅ | ✅ | ✅ |
+| RemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+| ResNet | ❌ | ❌ | ✅ | ❌ | ✅ |
+| RetriBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
+| RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ |
+| RoFormer | ✅ | ✅ | ✅ | ✅ | ✅ |
+| SegFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
+| SEW | ❌ | ❌ | ✅ | ❌ | ❌ |
+| SEW-D | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Speech Encoder decoder | ❌ | ❌ | ✅ | ❌ | ✅ |
+| Speech2Text | ✅ | ❌ | ✅ | ✅ | ❌ |
+| Speech2Text2 | ✅ | ❌ | ❌ | ❌ | ❌ |
+| Splinter | ✅ | ✅ | ✅ | ❌ | ❌ |
+| SqueezeBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
+| Swin | ❌ | ❌ | ✅ | ❌ | ❌ |
+| T5 | ✅ | ✅ | ✅ | ✅ | ✅ |
+| TAPAS | ✅ | ❌ | ✅ | ✅ | ❌ |
+| TAPEX | ✅ | ✅ | ✅ | ✅ | ✅ |
+| Transformer-XL | ✅ | ❌ | ✅ | ✅ | ❌ |
+| TrOCR | ❌ | ❌ | ✅ | ❌ | ❌ |
+| UniSpeech | ❌ | ❌ | ✅ | ❌ | ❌ |
+| UniSpeechSat | ❌ | ❌ | ✅ | ❌ | ❌ |
+| VAN | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ViLT | ❌ | ❌ | ✅ | ❌ | ❌ |
+| Vision Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅ |
+| VisionTextDualEncoder | ❌ | ❌ | ✅ | ❌ | ✅ |
+| VisualBert | ❌ | ❌ | ✅ | ❌ | ❌ |
+| ViT | ❌ | ❌ | ✅ | ✅ | ✅ |
+| ViTMAE | ❌ | ❌ | ✅ | ✅ | ❌ |
+| Wav2Vec2 | ✅ | ❌ | ✅ | ✅ | ✅ |
+| WavLM | ❌ | ❌ | ✅ | ❌ | ❌ |
+| XGLM | ✅ | ✅ | ✅ | ❌ | ✅ |
+| XLM | ✅ | ❌ | ✅ | ✅ | ❌ |
+| XLM-RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ |
+| XLM-RoBERTa-XL | ❌ | ❌ | ✅ | ❌ | ❌ |
+| XLMProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
+| XLNet | ✅ | ✅ | ✅ | ✅ | ❌ |
+| YOSO | ❌ | ❌ | ✅ | ❌ | ❌ |
+
+
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/installation.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/installation.md
new file mode 100644
index 0000000000000000000000000000000000000000..f548736589ac0d81bc7ebe2d73d60652ce03040b
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/pt/installation.md
@@ -0,0 +1,262 @@
+
+
+# Guia de Instalação
+
+Neste guia poderá encontrar informações para a instalação do 🤗 Transformers para qualquer biblioteca de
+Machine Learning com a qual esteja a trabalhar. Além disso, poderá encontrar informações sobre como gerar cachês e
+configurar o 🤗 Transformers para execução em modo offline (opcional).
+
+🤗 Transformers foi testado com Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, e Flax. Para instalar a biblioteca de
+deep learning com que deseja trabalhar, siga as instruções correspondentes listadas a seguir:
+
+* [PyTorch](https://pytorch.org/get-started/locally/)
+* [TensorFlow 2.0](https://www.tensorflow.org/install/pip)
+* [Flax](https://flax.readthedocs.io/en/latest/)
+
+## Instalação pelo Pip
+
+É sugerido instalar o 🤗 Transformers num [ambiente virtual](https://docs.python.org/3/library/venv.html). Se precisar
+de mais informações sobre ambientes virtuais em Python, consulte este [guia](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+Um ambiente virtual facilitará a manipulação e organização de projetos e evita problemas de compatibilidade entre dependências.
+
+Comece criando um ambiente virtual no diretório do seu projeto:
+
+```bash
+python -m venv .env
+```
+
+E para ativar o ambiente virtual:
+
+```bash
+source .env/bin/activate
+```
+
+Agora É possível instalar o 🤗 Transformers com o comando a seguir:
+
+```bash
+pip install transformers
+```
+
+Somente para a CPU, é possível instalar o 🤗 Transformers e a biblioteca de deep learning respectiva apenas numa linha.
+
+Por exemplo, para instalar o 🤗 Transformers e o PyTorch, digite:
+
+```bash
+pip install transformers[torch]
+```
+
+🤗 Transformers e TensorFlow 2.0:
+
+```bash
+pip install transformers[tf-cpu]
+```
+
+🤗 Transformers e Flax:
+
+```bash
+pip install transformers[flax]
+```
+
+Por último, verifique se o 🤗 Transformers foi instalado com sucesso usando o seguinte comando para baixar um modelo pré-treinado:
+
+```bash
+python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"
+```
+
+Em seguida, imprima um rótulo e sua pontuação:
+
+```bash
+[{'label': 'POSITIVE', 'score': 0.9998704791069031}]
+```
+
+## Instalação usando a fonte
+
+Para instalar o 🤗 Transformers a partir da fonte use o seguinte comando:
+
+```bash
+pip install git+https://github.com/huggingface/transformers
+```
+
+O comando acima instalará a versão `master` mais atual em vez da última versão estável. A versão `master` é útil para
+utilizar os últimos updates contidos em 🤗 Transformers. Por exemplo, um erro recente pode ter sido corrigido somente
+após a última versão estável, antes que houvesse um novo lançamento. No entanto, há a possibilidade que a versão `master` não esteja estável.
+A equipa trata de mantér a versão `master` operacional e a maioria dos erros são resolvidos em poucas horas ou dias.
+Se encontrar quaisquer problemas, por favor abra um [Issue](https://github.com/huggingface/transformers/issues) para que o
+mesmo possa ser corrigido o mais rápido possível.
+
+Verifique que o 🤗 Transformers está instalado corretamente usando o seguinte comando:
+
+```bash
+python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"
+```
+
+## Instalação editável
+
+Uma instalação editável será necessária caso desejas um dos seguintes:
+* Usar a versão `master` do código fonte.
+* Contribuir ao 🤗 Transformers e precisa testar mudanças ao código.
+
+Para tal, clone o repositório e instale o 🤗 Transformers com os seguintes comandos:
+
+```bash
+git clone https://github.com/huggingface/transformers.git
+cd transformers
+pip install -e .
+```
+
+Estes comandos vão ligar o diretório para o qual foi clonado o repositório ao caminho de bibliotecas do Python.
+O Python agora buscará dentro dos arquivos que foram clonados além dos caminhos normais da biblioteca.
+Por exemplo, se os pacotes do Python se encontram instalados no caminho `~/anaconda3/envs/main/lib/python3.7/site-packages/`,
+o Python também buscará módulos no diretório onde clonamos o repositório `~/transformers/`.
+
+
+
+
+Em seguida, mude seu clone atual dos 🤗 Transformers para uma versão específica, como v3.5.1, por exemplo:
+
+```bash
+git checkout tags/v3.5.1
+```
+
+Depois de configurar a versão correta da biblioteca, navegue até a pasta de exemplo de sua escolha e instale os requisitos específicos do exemplo:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Executando um script
+
+Exemplos para versões antigas dos 🤗 Transformers
+ ++🖼️ **కంప్యూటర్ విషయం**: చిత్రం వర్గీకరణ, వస్త్రం గుర్తువు, మరియు విభజన.
+🗣️ **ఆడియో**: స్వయంచలన ప్రసంగాన్ని గుర్తుచేసేందుకు, ఆడియో వర్గీకరణ.
+🐙 **బహుమూలిక**: పట్టి ప్రశ్న సంవాద, ఆప్టికల్ సిఫర్ గుర్తువు, డాక్యుమెంట్లు స్క్యాన్ చేసినంతగా సమాచార పొందడం, వీడియో వర్గీకరణ, మరియు దృశ్య ప్రశ్న సంవాద. + +🤗 ట్రాన్స్ఫార్మర్స్ పైన మద్దతు చేస్తుంది పైన తొలగించడానికి పైన పైన పైన ప్రోగ్రామ్లో మోడల్ను శిక్షించండి, మరియు అన్ని ప్రాథమిక యొక్కడా ఇన్ఫరెన్స్ కోసం లోడ్ చేయండి. మో + +డల్లు కూడా ప్రొడక్షన్ వాతావరణాలలో వాడుకోవడానికి ONNX మరియు TorchScript వంటి ఆకృతులకు ఎగుమతి చేయవచ్చు. + +ఈరువులకు [హబ్](https://huggingface.co/models), [ఫోరం](https://discuss.huggingface.co/), లేదా [డిస్కార్డ్](https://discord.com/invite/JfAtkvEtRb) లో ఈ పెద్ద సముదాయంలో చేరండి! + +## మీరు హగ్గింగ్ ఫేస్ టీమ్ నుండి అనుకూల మద్దతు కోసం చూస్తున్నట్లయితే + + +
+
+
+## విషయాలు
+
+డాక్యుమెంటేషన్ ఐదు విభాగాలుగా నిర్వహించబడింది:
+
+- **ప్రారంభించండి** లైబ్రరీ యొక్క శీఘ్ర పర్యటన మరియు రన్నింగ్ కోసం ఇన్స్టాలేషన్ సూచనలను అందిస్తుంది.
+- **ట్యుటోరియల్స్** మీరు అనుభవశూన్యుడు అయితే ప్రారంభించడానికి గొప్ప ప్రదేశం. మీరు లైబ్రరీని ఉపయోగించడం ప్రారంభించడానికి అవసరమైన ప్రాథమిక నైపుణ్యాలను పొందడానికి ఈ విభాగం మీకు సహాయం చేస్తుంది.
+- **హౌ-టు-గైడ్లు** లాంగ్వేజ్ మోడలింగ్ కోసం ప్రిట్రైన్డ్ మోడల్ని ఫైన్ట్యూన్ చేయడం లేదా కస్టమ్ మోడల్ను ఎలా వ్రాయాలి మరియు షేర్ చేయాలి వంటి నిర్దిష్ట లక్ష్యాన్ని ఎలా సాధించాలో మీకు చూపుతాయి.
+- **కాన్సెప్చువల్ గైడ్స్** మోడల్లు, టాస్క్లు మరియు 🤗 ట్రాన్స్ఫార్మర్ల డిజైన్ ఫిలాసఫీ వెనుక ఉన్న అంతర్లీన భావనలు మరియు ఆలోచనల గురించి మరింత చర్చ మరియు వివరణను అందిస్తుంది.
+- **API** అన్ని తరగతులు మరియు విధులను వివరిస్తుంది:
+
+ - **ప్రధాన తరగతులు** కాన్ఫిగరేషన్, మోడల్, టోకెనైజర్ మరియు పైప్లైన్ వంటి అత్యంత ముఖ్యమైన తరగతులను వివరిస్తుంది.
+ - **మోడల్స్** లైబ్రరీలో అమలు చేయబడిన ప్రతి మోడల్కు సంబంధించిన తరగతులు మరియు విధులను వివరిస్తుంది.
+ - **అంతర్గత సహాయకులు** అంతర్గతంగా ఉపయోగించే యుటిలిటీ క్లాస్లు మరియు ఫంక్షన్ల వివరాలు.
+
+## మద్దతు ఉన్న నమూనాలు మరియు ఫ్రేమ్వర్క్లు
+
+దిగువన ఉన్న పట్టిక ఆ ప్రతి మోడల్కు పైథాన్ కలిగి ఉన్నా లైబ్రరీలో ప్రస్తుత మద్దతును సూచిస్తుంది
+టోకెనైజర్ ("నెమ్మదిగా" అని పిలుస్తారు). Jax (ద్వారా
+ఫ్లాక్స్), పైటార్చ్ మరియు/లేదా టెన్సర్ఫ్లో.
+
+
+
+| Model | PyTorch support | TensorFlow support | Flax Support |
+|:------------------------------------------------------------------------:|:---------------:|:------------------:|:------------:|
+| [ALBERT](model_doc/albert) | ✅ | ✅ | ✅ |
+| [ALIGN](model_doc/align) | ✅ | ❌ | ❌ |
+| [AltCLIP](model_doc/altclip) | ✅ | ❌ | ❌ |
+| [Audio Spectrogram Transformer](model_doc/audio-spectrogram-transformer) | ✅ | ❌ | ❌ |
+| [Autoformer](model_doc/autoformer) | ✅ | ❌ | ❌ |
+| [Bark](model_doc/bark) | ✅ | ❌ | ❌ |
+| [BART](model_doc/bart) | ✅ | ✅ | ✅ |
+| [BARThez](model_doc/barthez) | ✅ | ✅ | ✅ |
+| [BARTpho](model_doc/bartpho) | ✅ | ✅ | ✅ |
+| [BEiT](model_doc/beit) | ✅ | ❌ | ✅ |
+| [BERT](model_doc/bert) | ✅ | ✅ | ✅ |
+| [Bert Generation](model_doc/bert-generation) | ✅ | ❌ | ❌ |
+| [BertJapanese](model_doc/bert-japanese) | ✅ | ✅ | ✅ |
+| [BERTweet](model_doc/bertweet) | ✅ | ✅ | ✅ |
+| [BigBird](model_doc/big_bird) | ✅ | ❌ | ✅ |
+| [BigBird-Pegasus](model_doc/bigbird_pegasus) | ✅ | ❌ | ❌ |
+| [BioGpt](model_doc/biogpt) | ✅ | ❌ | ❌ |
+| [BiT](model_doc/bit) | ✅ | ❌ | ❌ |
+| [Blenderbot](model_doc/blenderbot) | ✅ | ✅ | ✅ |
+| [BlenderbotSmall](model_doc/blenderbot-small) | ✅ | ✅ | ✅ |
+| [BLIP](model_doc/blip) | ✅ | ✅ | ❌ |
+| [BLIP-2](model_doc/blip-2) | ✅ | ❌ | ❌ |
+| [BLOOM](model_doc/bloom) | ✅ | ❌ | ✅ |
+| [BORT](model_doc/bort) | ✅ | ✅ | ✅ |
+| [BridgeTower](model_doc/bridgetower) | ✅ | ❌ | ❌ |
+| [BROS](model_doc/bros) | ✅ | ❌ | ❌ |
+| [ByT5](model_doc/byt5) | ✅ | ✅ | ✅ |
+| [CamemBERT](model_doc/camembert) | ✅ | ✅ | ❌ |
+| [CANINE](model_doc/canine) | ✅ | ❌ | ❌ |
+| [Chinese-CLIP](model_doc/chinese_clip) | ✅ | ❌ | ❌ |
+| [CLAP](model_doc/clap) | ✅ | ❌ | ❌ |
+| [CLIP](model_doc/clip) | ✅ | ✅ | ✅ |
+| [CLIPSeg](model_doc/clipseg) | ✅ | ❌ | ❌ |
+| [CodeGen](model_doc/codegen) | ✅ | ❌ | ❌ |
+| [CodeLlama](model_doc/code_llama) | ✅ | ❌ | ❌ |
+| [Conditional DETR](model_doc/conditional_detr) | ✅ | ❌ | ❌ |
+| [ConvBERT](model_doc/convbert) | ✅ | ✅ | ❌ |
+| [ConvNeXT](model_doc/convnext) | ✅ | ✅ | ❌ |
+| [ConvNeXTV2](model_doc/convnextv2) | ✅ | ❌ | ❌ |
+| [CPM](model_doc/cpm) | ✅ | ✅ | ✅ |
+| [CPM-Ant](model_doc/cpmant) | ✅ | ❌ | ❌ |
+| [CTRL](model_doc/ctrl) | ✅ | ✅ | ❌ |
+| [CvT](model_doc/cvt) | ✅ | ✅ | ❌ |
+| [Data2VecAudio](model_doc/data2vec) | ✅ | ❌ | ❌ |
+| [Data2VecText](model_doc/data2vec) | ✅ | ❌ | ❌ |
+| [Data2VecVision](model_doc/data2vec) | ✅ | ✅ | ❌ |
+| [DeBERTa](model_doc/deberta) | ✅ | ✅ | ❌ |
+| [DeBERTa-v2](model_doc/deberta-v2) | ✅ | ✅ | ❌ |
+| [Decision Transformer](model_doc/decision_transformer) | ✅ | ❌ | ❌ |
+| [Deformable DETR](model_doc/deformable_detr) | ✅ | ❌ | ❌ |
+| [DeiT](model_doc/deit) | ✅ | ✅ | ❌ |
+| [DePlot](model_doc/deplot) | ✅ | ❌ | ❌ |
+| [DETA](model_doc/deta) | ✅ | ❌ | ❌ |
+| [DETR](model_doc/detr) | ✅ | ❌ | ❌ |
+| [DialoGPT](model_doc/dialogpt) | ✅ | ✅ | ✅ |
+| [DiNAT](model_doc/dinat) | ✅ | ❌ | ❌ |
+| [DINOv2](model_doc/dinov2) | ✅ | ❌ | ❌ |
+| [DistilBERT](model_doc/distilbert) | ✅ | ✅ | ✅ |
+| [DiT](model_doc/dit) | ✅ | ❌ | ✅ |
+| [DonutSwin](model_doc/donut) | ✅ | ❌ | ❌ |
+| [DPR](model_doc/dpr) | ✅ | ✅ | ❌ |
+| [DPT](model_doc/dpt) | ✅ | ❌ | ❌ |
+| [EfficientFormer](model_doc/efficientformer) | ✅ | ✅ | ❌ |
+| [EfficientNet](model_doc/efficientnet) | ✅ | ❌ | ❌ |
+| [ELECTRA](model_doc/electra) | ✅ | ✅ | ✅ |
+| [EnCodec](model_doc/encodec) | ✅ | ❌ | ❌ |
+| [Encoder decoder](model_doc/encoder-decoder) | ✅ | ✅ | ✅ |
+| [ERNIE](model_doc/ernie) | ✅ | ❌ | ❌ |
+| [ErnieM](model_doc/ernie_m) | ✅ | ❌ | ❌ |
+| [ESM](model_doc/esm) | ✅ | ✅ | ❌ |
+| [FairSeq Machine-Translation](model_doc/fsmt) | ✅ | ❌ | ❌ |
+| [Falcon](model_doc/falcon) | ✅ | ❌ | ❌ |
+| [FLAN-T5](model_doc/flan-t5) | ✅ | ✅ | ✅ |
+| [FLAN-UL2](model_doc/flan-ul2) | ✅ | ✅ | ✅ |
+| [FlauBERT](model_doc/flaubert) | ✅ | ✅ | ❌ |
+| [FLAVA](model_doc/flava) | ✅ | ❌ | ❌ |
+| [FNet](model_doc/fnet) | ✅ | ❌ | ❌ |
+| [FocalNet](model_doc/focalnet) | ✅ | ❌ | ❌ |
+| [Funnel Transformer](model_doc/funnel) | ✅ | ✅ | ❌ |
+| [GIT](model_doc/git) | ✅ | ❌ | ❌ |
+| [GLPN](model_doc/glpn) | ✅ | ❌ | ❌ |
+| [GPT Neo](model_doc/gpt_neo) | ✅ | ❌ | ✅ |
+| [GPT NeoX](model_doc/gpt_neox) | ✅ | ❌ | ❌ |
+| [GPT NeoX Japanese](model_doc/gpt_neox_japanese) | ✅ | ❌ | ❌ |
+| [GPT-J](model_doc/gptj) | ✅ | ✅ | ✅ |
+| [GPT-Sw3](model_doc/gpt-sw3) | ✅ | ✅ | ✅ |
+| [GPTBigCode](model_doc/gpt_bigcode) | ✅ | ❌ | ❌ |
+| [GPTSAN-japanese](model_doc/gptsan-japanese) | ✅ | ❌ | ❌ |
+| [Graphormer](model_doc/graphormer) | ✅ | ❌ | ❌ |
+| [GroupViT](model_doc/groupvit) | ✅ | ✅ | ❌ |
+| [HerBERT](model_doc/herbert) | ✅ | ✅ | ✅ |
+| [Hubert](model_doc/hubert) | ✅ | ✅ | ❌ |
+| [I-BERT](model_doc/ibert) | ✅ | ❌ | ❌ |
+| [IDEFICS](model_doc/idefics) | ✅ | ❌ | ❌ |
+| [ImageGPT](model_doc/imagegpt) | ✅ | ❌ | ❌ |
+| [Informer](model_doc/informer) | ✅ | ❌ | ❌ |
+| [InstructBLIP](model_doc/instructblip) | ✅ | ❌ | ❌ |
+| [Jukebox](model_doc/jukebox) | ✅ | ❌ | ❌ |
+| [LayoutLM](model_doc/layoutlm) | ✅ | ✅ | ❌ |
+| [LayoutLMv2](model_doc/layoutlmv2) | ✅ | ❌ | ❌ |
+| [LayoutLMv3](model_doc/layoutlmv3) | ✅ | ✅ | ❌ |
+| [LayoutXLM](model_doc/layoutxlm) | ✅ | ❌ | ❌ |
+| [LED](model_doc/led) | ✅ | ✅ | ❌ |
+| [LeViT](model_doc/levit) | ✅ | ❌ | ❌ |
+| [LiLT](model_doc/lilt) | ✅ | ❌ | ❌ |
+| [LLaMA](model_doc/llama) | ✅ | ❌ | ❌ |
+| [Llama2](model_doc/llama2) | ✅ | ❌ | ❌ |
+| [Longformer](model_doc/longformer) | ✅ | ✅ | ❌ |
+| [LongT5](model_doc/longt5) | ✅ | ❌ | ✅ |
+| [LUKE](model_doc/luke) | ✅ | ❌ | ❌ |
+| [LXMERT](model_doc/lxmert) | ✅ | ✅ | ❌ |
+| [M-CTC-T](model_doc/mctct) | ✅ | ❌ | ❌ |
+| [M2M100](model_doc/m2m_100) | ✅ | ❌ | ❌ |
+| [Marian](model_doc/marian) | ✅ | ✅ | ✅ |
+| [MarkupLM](model_doc/markuplm) | ✅ | ❌ | ❌ |
+| [Mask2Former](model_doc/mask2former) | ✅ | ❌ | ❌ |
+| [MaskFormer](model_doc/maskformer) | ✅ | ❌ | ❌ |
+| [MatCha](model_doc/matcha) | ✅ | ❌ | ❌ |
+| [mBART](model_doc/mbart) | ✅ | ✅ | ✅ |
+| [mBART-50](model_doc/mbart50) | ✅ | ✅ | ✅ |
+| [MEGA](model_doc/mega) | ✅ | ❌ | ❌ |
+| [Megatron-BERT](model_doc/megatron-bert) | ✅ | ❌ | ❌ |
+| [Megatron-GPT2](model_doc/megatron_gpt2) | ✅ | ✅ | ✅ |
+| [MGP-STR](model_doc/mgp-str) | ✅ | ❌ | ❌ |
+| [Mistral](model_doc/mistral) | ✅ | ❌ | ❌ |
+| [mLUKE](model_doc/mluke) | ✅ | ❌ | ❌ |
+| [MMS](model_doc/mms) | ✅ | ✅ | ✅ |
+| [MobileBERT](model_doc/mobilebert) | ✅ | ✅ | ❌ |
+| [MobileNetV1](model_doc/mobilenet_v1) | ✅ | ❌ | ❌ |
+| [MobileNetV2](model_doc/mobilenet_v2) | ✅ | ❌ | ❌ |
+| [MobileViT](model_doc/mobilevit) | ✅ | ✅ | ❌ |
+| [MobileViTV2](model_doc/mobilevitv2) | ✅ | ❌ | ❌ |
+| [MPNet](model_doc/mpnet) | ✅ | ✅ | ❌ |
+| [MPT](model_doc/mpt) | ✅ | ❌ | ❌ |
+| [MRA](model_doc/mra) | ✅ | ❌ | ❌ |
+| [MT5](model_doc/mt5) | ✅ | ✅ | ✅ |
+| [MusicGen](model_doc/musicgen) | ✅ | ❌ | ❌ |
+| [MVP](model_doc/mvp) | ✅ | ❌ | ❌ |
+| [NAT](model_doc/nat) | ✅ | ❌ | ❌ |
+| [Nezha](model_doc/nezha) | ✅ | ❌ | ❌ |
+| [NLLB](model_doc/nllb) | ✅ | ❌ | ❌ |
+| [NLLB-MOE](model_doc/nllb-moe) | ✅ | ❌ | ❌ |
+| [Nougat](model_doc/nougat) | ✅ | ✅ | ✅ |
+| [Nyströmformer](model_doc/nystromformer) | ✅ | ❌ | ❌ |
+| [OneFormer](model_doc/oneformer) | ✅ | ❌ | ❌ |
+| [OpenAI GPT](model_doc/openai-gpt) | ✅ | ✅ | ❌ |
+| [OpenAI GPT-2](model_doc/gpt2) | ✅ | ✅ | ✅ |
+| [OpenLlama](model_doc/open-llama) | ✅ | ❌ | ❌ |
+| [OPT](model_doc/opt) | ✅ | ✅ | ✅ |
+| [OWL-ViT](model_doc/owlvit) | ✅ | ❌ | ❌ |
+| [Pegasus](model_doc/pegasus) | ✅ | ✅ | ✅ |
+| [PEGASUS-X](model_doc/pegasus_x) | ✅ | ❌ | ❌ |
+| [Perceiver](model_doc/perceiver) | ✅ | ❌ | ❌ |
+| [Persimmon](model_doc/persimmon) | ✅ | ❌ | ❌ |
+| [PhoBERT](model_doc/phobert) | ✅ | ✅ | ✅ |
+| [Pix2Struct](model_doc/pix2struct) | ✅ | ❌ | ❌ |
+| [PLBart](model_doc/plbart) | ✅ | ❌ | ❌ |
+| [PoolFormer](model_doc/poolformer) | ✅ | ❌ | ❌ |
+| [Pop2Piano](model_doc/pop2piano) | ✅ | ❌ | ❌ |
+| [ProphetNet](model_doc/prophetnet) | ✅ | ❌ | ❌ |
+| [PVT](model_doc/pvt) | ✅ | ❌ | ❌ |
+| [QDQBert](model_doc/qdqbert) | ✅ | ❌ | ❌ |
+| [RAG](model_doc/rag) | ✅ | ✅ | ❌ |
+| [REALM](model_doc/realm) | ✅ | ❌ | ❌ |
+| [Reformer](model_doc/reformer) | ✅ | ❌ | ❌ |
+| [RegNet](model_doc/regnet) | ✅ | ✅ | ✅ |
+| [RemBERT](model_doc/rembert) | ✅ | ✅ | ❌ |
+| [ResNet](model_doc/resnet) | ✅ | ✅ | ✅ |
+| [RetriBERT](model_doc/retribert) | ✅ | ❌ | ❌ |
+| [RoBERTa](model_doc/roberta) | ✅ | ✅ | ✅ |
+| [RoBERTa-PreLayerNorm](model_doc/roberta-prelayernorm) | ✅ | ✅ | ✅ |
+| [RoCBert](model_doc/roc_bert) | ✅ | ❌ | ❌ |
+| [RoFormer](model_doc/roformer) | ✅ | ✅ | ✅ |
+| [RWKV](model_doc/rwkv) | ✅ | ❌ | ❌ |
+| [SAM](model_doc/sam) | ✅ | ✅ | ❌ |
+| [SegFormer](model_doc/segformer) | ✅ | ✅ | ❌ |
+| [SEW](model_doc/sew) | ✅ | ❌ | ❌ |
+| [SEW-D](model_doc/sew-d) | ✅ | ❌ | ❌ |
+| [Speech Encoder decoder](model_doc/speech-encoder-decoder) | ✅ | ❌ | ✅ |
+| [Speech2Text](model_doc/speech_to_text) | ✅ | ✅ | ❌ |
+| [SpeechT5](model_doc/speecht5) | ✅ | ❌ | ❌ |
+| [Splinter](model_doc/splinter) | ✅ | ❌ | ❌ |
+| [SqueezeBERT](model_doc/squeezebert) | ✅ | ❌ | ❌ |
+| [SwiftFormer](model_doc/swiftformer) | ✅ | ❌ | ❌ |
+| [Swin Transformer](model_doc/swin) | ✅ | ✅ | ❌ |
+| [Swin Transformer V2](model_doc/swinv2) | ✅ | ❌ | ❌ |
+| [Swin2SR](model_doc/swin2sr) | ✅ | ❌ | ❌ |
+| [SwitchTransformers](model_doc/switch_transformers) | ✅ | ❌ | ❌ |
+| [T5](model_doc/t5) | ✅ | ✅ | ✅ |
+| [T5v1.1](model_doc/t5v1.1) | ✅ | ✅ | ✅ |
+| [Table Transformer](model_doc/table-transformer) | ✅ | ❌ | ❌ |
+| [TAPAS](model_doc/tapas) | ✅ | ✅ | ❌ |
+| [TAPEX](model_doc/tapex) | ✅ | ✅ | ✅ |
+| [Time Series Transformer](model_doc/time_series_transformer) | ✅ | ❌ | ❌ |
+| [TimeSformer](model_doc/timesformer) | ✅ | ❌ | ❌ |
+| [Trajectory Transformer](model_doc/trajectory_transformer) | ✅ | ❌ | ❌ |
+| [Transformer-XL](model_doc/transfo-xl) | ✅ | ✅ | ❌ |
+| [TrOCR](model_doc/trocr) | ✅ | ❌ | ❌ |
+| [TVLT](model_doc/tvlt) | ✅ | ❌ | ❌ |
+| [UL2](model_doc/ul2) | ✅ | ✅ | ✅ |
+| [UMT5](model_doc/umt5) | ✅ | ❌ | ❌ |
+| [UniSpeech](model_doc/unispeech) | ✅ | ❌ | ❌ |
+| [UniSpeechSat](model_doc/unispeech-sat) | ✅ | ❌ | ❌ |
+| [UPerNet](model_doc/upernet) | ✅ | ❌ | ❌ |
+| [VAN](model_doc/van) | ✅ | ❌ | ❌ |
+| [VideoMAE](model_doc/videomae) | ✅ | ❌ | ❌ |
+| [ViLT](model_doc/vilt) | ✅ | ❌ | ❌ |
+| [Vision Encoder decoder](model_doc/vision-encoder-decoder) | ✅ | ✅ | ✅ |
+| [VisionTextDualEncoder](model_doc/vision-text-dual-encoder) | ✅ | ✅ | ✅ |
+| [VisualBERT](model_doc/visual_bert) | ✅ | ❌ | ❌ |
+| [ViT](model_doc/vit) | ✅ | ✅ | ✅ |
+| [ViT Hybrid](model_doc/vit_hybrid) | ✅ | ❌ | ❌ |
+| [VitDet](model_doc/vitdet) | ✅ | ❌ | ❌ |
+| [ViTMAE](model_doc/vit_mae) | ✅ | ✅ | ❌ |
+| [ViTMatte](model_doc/vitmatte) | ✅ | ❌ | ❌ |
+| [ViTMSN](model_doc/vit_msn) | ✅ | ❌ | ❌ |
+| [VITS](model_doc/vits) | ✅ | ❌ | ❌ |
+| [ViViT](model_doc/vivit) | ✅ | ❌ | ❌ |
+| [Wav2Vec2](model_doc/wav2vec2) | ✅ | ✅ | ✅ |
+| [Wav2Vec2-Conformer](model_doc/wav2vec2-conformer) | ✅ | ❌ | ❌ |
+| [Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme) | ✅ | ✅ | ✅ |
+| [WavLM](model_doc/wavlm) | ✅ | ❌ | ❌ |
+| [Whisper](model_doc/whisper) | ✅ | ✅ | ✅ |
+| [X-CLIP](model_doc/xclip) | ✅ | ❌ | ❌ |
+| [X-MOD](model_doc/xmod) | ✅ | ❌ | ❌ |
+| [XGLM](model_doc/xglm) | ✅ | ✅ | ✅ |
+| [XLM](model_doc/xlm) | ✅ | ✅ | ❌ |
+| [XLM-ProphetNet](model_doc/xlm-prophetnet) | ✅ | ❌ | ❌ |
+| [XLM-RoBERTa](model_doc/xlm-roberta) | ✅ | ✅ | ✅ |
+| [XLM-RoBERTa-XL](model_doc/xlm-roberta-xl) | ✅ | ❌ | ❌ |
+| [XLM-V](model_doc/xlm-v) | ✅ | ✅ | ✅ |
+| [XLNet](model_doc/xlnet) | ✅ | ✅ | ❌ |
+| [XLS-R](model_doc/xls_r) | ✅ | ✅ | ✅ |
+| [XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2) | ✅ | ✅ | ✅ |
+| [YOLOS](model_doc/yolos) | ✅ | ❌ | ❌ |
+| [YOSO](model_doc/yoso) | ✅ | ❌ | ❌ |
+
+
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/te/quicktour.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/te/quicktour.md
new file mode 100644
index 0000000000000000000000000000000000000000..6045b673d2d3d0e71f097ff6666f3dd9bb9a5f06
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/te/quicktour.md
@@ -0,0 +1,557 @@
+
+
+# శీఘ్ర పర్యటన
+
+[[ఓపెన్-ఇన్-కోలాబ్]]
+
+🤗 ట్రాన్స్ఫార్మర్లతో లేచి పరుగెత్తండి! మీరు డెవలపర్ అయినా లేదా రోజువారీ వినియోగదారు అయినా, ఈ శీఘ్ర పర్యటన మీకు ప్రారంభించడానికి సహాయం చేస్తుంది మరియు [`pipeline`] అనుమితి కోసం ఎలా ఉపయోగించాలో మీకు చూపుతుంది, [AutoClass](./model_doc/auto) తో ప్రీట్రైన్డ్ మోడల్ మరియు ప్రిప్రాసెసర్/ ఆటో, మరియు PyTorch లేదా TensorFlowతో మోడల్కు త్వరగా శిక్షణ ఇవ్వండి. మీరు ఒక అనుభవశూన్యుడు అయితే, ఇక్కడ పరిచయం చేయబడిన భావనల గురించి మరింత లోతైన వివరణల కోసం మా ట్యుటోరియల్స్ లేదా [course](https://huggingface.co/course/chapter1/1)ని తనిఖీ చేయమని మేము సిఫార్సు చేస్తున్నాము.
+
+మీరు ప్రారంభించడానికి ముందు, మీరు అవసరమైన అన్ని లైబ్రరీలను ఇన్స్టాల్ చేశారని నిర్ధారించుకోండి:
+
+```bash
+!pip install transformers datasets evaluate accelerate
+```
+
+మీరు మీ ప్రాధాన్య యంత్ర అభ్యాస ఫ్రేమ్వర్క్ను కూడా ఇన్స్టాల్ చేయాలి:
+
++🖼️ **Bilgisayarlı Görü**: görüntü sınıflandırma, nesne tespiti ve bölümleme (segmentation).
+🗣️ **Ses**: otomatik konuşma tanıma ve ses sınıflandırma.
+🐙 **Çoklu Model**: tablo soru cevaplama, optik karakter tanıma, taranmış belgelerden bilgi çıkarma, video sınıflandırma ve görsel soru cevaplama. + +🤗 Transformers, PyTorch, TensorFlow ve JAX arasında çerçeve (framework) uyumluluğu sağlar. Bu, bir modelin yaşam döngüsünün her aşamasında farklı bir çerçeve kullanma esnekliği sunar; bir çerçevede üç satır kodla bir modeli eğitebilir ve başka bir çerçevede tahminleme için kullanabilirsiniz. Modeller ayrıca üretim ortamlarında kullanılmak üzere ONNX ve TorchScript gibi bir formata aktarılabilir. + +Büyüyen topluluğa [Hub](https://huggingface.co/models), [Forum](https://discuss.huggingface.co/) veya [Discord](https://discord.com/invite/JfAtkvEtRb) üzerinden katılabilirsiniz! + +## Hugging Face ekibinden özel destek arıyorsanız + + +
+
+
+## İçindekiler
+
+Dokümantasyon, beş bölüme ayrılmıştır:
+
+- **BAŞLARKEN**, kütüphanenin hızlı bir turunu ve çalışmaya başlamak için kurulum talimatlarını sağlar.
+- **ÖĞRETİCİLER**, başlangıç yapmak için harika bir yerdir. Bu bölüm, kütüphane kullanmaya başlamak için ihtiyacınız olan temel becerileri kazanmanıza yardımcı olacaktır.
+- **NASIL YAPILIR KILAVUZLARI**, önceden eğitilmiş bir modele dil modellemesi için ince ayar (fine-tuning) yapmak veya özel bir model yazmak, ve paylaşmak gibi belirli bir hedefe nasıl ulaşılacağını gösterir.
+- **KAVRAMSAL REHBERLER**, modellerin, görevlerin ve 🤗 Transformers tasarım felsefesinin temel kavramları ve fikirleri hakkında daha fazla tartışma ve açıklama sunar.
+- **API** tüm sınıfları (class) ve fonksiyonları (functions) açıklar:
+
+ - **ANA SINIFLAR**, yapılandırma, model, tokenizer ve pipeline gibi en önemli sınıfları (classes) ayrıntılandırır.
+ - **MODELLER**, kütüphanede kullanılan her modelle ilgili sınıfları ve fonksiyonları detaylı olarak inceler.
+ - **DAHİLİ YARDIMCILAR**, kullanılan yardımcı sınıfları ve fonksiyonları detaylı olarak inceler.
+
+## Desteklenen Modeller ve Çerçeveler
+
+Aşağıdaki tablo, her bir model için kütüphanede yer alan mevcut desteği temsil etmektedir. Her bir model için bir Python tokenizer'ına ("slow" olarak adlandırılır) sahip olup olmadıkları, 🤗 Tokenizers kütüphanesi tarafından desteklenen hızlı bir tokenizer'a sahip olup olmadıkları, Jax (Flax aracılığıyla), PyTorch ve/veya TensorFlow'da destek olup olmadıklarını göstermektedir.
+
+
+
+| Model | PyTorch support | TensorFlow support | Flax Support |
+|:------------------------------------------------------------------------:|:---------------:|:------------------:|:------------:|
+| [ALBERT](model_doc/albert) | ✅ | ✅ | ✅ |
+| [ALIGN](model_doc/align) | ✅ | ❌ | ❌ |
+| [AltCLIP](model_doc/altclip) | ✅ | ❌ | ❌ |
+| [Audio Spectrogram Transformer](model_doc/audio-spectrogram-transformer) | ✅ | ❌ | ❌ |
+| [Autoformer](model_doc/autoformer) | ✅ | ❌ | ❌ |
+| [Bark](model_doc/bark) | ✅ | ❌ | ❌ |
+| [BART](model_doc/bart) | ✅ | ✅ | ✅ |
+| [BARThez](model_doc/barthez) | ✅ | ✅ | ✅ |
+| [BARTpho](model_doc/bartpho) | ✅ | ✅ | ✅ |
+| [BEiT](model_doc/beit) | ✅ | ❌ | ✅ |
+| [BERT](model_doc/bert) | ✅ | ✅ | ✅ |
+| [Bert Generation](model_doc/bert-generation) | ✅ | ❌ | ❌ |
+| [BertJapanese](model_doc/bert-japanese) | ✅ | ✅ | ✅ |
+| [BERTweet](model_doc/bertweet) | ✅ | ✅ | ✅ |
+| [BigBird](model_doc/big_bird) | ✅ | ❌ | ✅ |
+| [BigBird-Pegasus](model_doc/bigbird_pegasus) | ✅ | ❌ | ❌ |
+| [BioGpt](model_doc/biogpt) | ✅ | ❌ | ❌ |
+| [BiT](model_doc/bit) | ✅ | ❌ | ❌ |
+| [Blenderbot](model_doc/blenderbot) | ✅ | ✅ | ✅ |
+| [BlenderbotSmall](model_doc/blenderbot-small) | ✅ | ✅ | ✅ |
+| [BLIP](model_doc/blip) | ✅ | ✅ | ❌ |
+| [BLIP-2](model_doc/blip-2) | ✅ | ❌ | ❌ |
+| [BLOOM](model_doc/bloom) | ✅ | ❌ | ✅ |
+| [BORT](model_doc/bort) | ✅ | ✅ | ✅ |
+| [BridgeTower](model_doc/bridgetower) | ✅ | ❌ | ❌ |
+| [BROS](model_doc/bros) | ✅ | ❌ | ❌ |
+| [ByT5](model_doc/byt5) | ✅ | ✅ | ✅ |
+| [CamemBERT](model_doc/camembert) | ✅ | ✅ | ❌ |
+| [CANINE](model_doc/canine) | ✅ | ❌ | ❌ |
+| [Chinese-CLIP](model_doc/chinese_clip) | ✅ | ❌ | ❌ |
+| [CLAP](model_doc/clap) | ✅ | ❌ | ❌ |
+| [CLIP](model_doc/clip) | ✅ | ✅ | ✅ |
+| [CLIPSeg](model_doc/clipseg) | ✅ | ❌ | ❌ |
+| [CodeGen](model_doc/codegen) | ✅ | ❌ | ❌ |
+| [CodeLlama](model_doc/code_llama) | ✅ | ❌ | ❌ |
+| [Conditional DETR](model_doc/conditional_detr) | ✅ | ❌ | ❌ |
+| [ConvBERT](model_doc/convbert) | ✅ | ✅ | ❌ |
+| [ConvNeXT](model_doc/convnext) | ✅ | ✅ | ❌ |
+| [ConvNeXTV2](model_doc/convnextv2) | ✅ | ❌ | ❌ |
+| [CPM](model_doc/cpm) | ✅ | ✅ | ✅ |
+| [CPM-Ant](model_doc/cpmant) | ✅ | ❌ | ❌ |
+| [CTRL](model_doc/ctrl) | ✅ | ✅ | ❌ |
+| [CvT](model_doc/cvt) | ✅ | ✅ | ❌ |
+| [Data2VecAudio](model_doc/data2vec) | ✅ | ❌ | ❌ |
+| [Data2VecText](model_doc/data2vec) | ✅ | ❌ | ❌ |
+| [Data2VecVision](model_doc/data2vec) | ✅ | ✅ | ❌ |
+| [DeBERTa](model_doc/deberta) | ✅ | ✅ | ❌ |
+| [DeBERTa-v2](model_doc/deberta-v2) | ✅ | ✅ | ❌ |
+| [Decision Transformer](model_doc/decision_transformer) | ✅ | ❌ | ❌ |
+| [Deformable DETR](model_doc/deformable_detr) | ✅ | ❌ | ❌ |
+| [DeiT](model_doc/deit) | ✅ | ✅ | ❌ |
+| [DePlot](model_doc/deplot) | ✅ | ❌ | ❌ |
+| [DETA](model_doc/deta) | ✅ | ❌ | ❌ |
+| [DETR](model_doc/detr) | ✅ | ❌ | ❌ |
+| [DialoGPT](model_doc/dialogpt) | ✅ | ✅ | ✅ |
+| [DiNAT](model_doc/dinat) | ✅ | ❌ | ❌ |
+| [DINOv2](model_doc/dinov2) | ✅ | ❌ | ❌ |
+| [DistilBERT](model_doc/distilbert) | ✅ | ✅ | ✅ |
+| [DiT](model_doc/dit) | ✅ | ❌ | ✅ |
+| [DonutSwin](model_doc/donut) | ✅ | ❌ | ❌ |
+| [DPR](model_doc/dpr) | ✅ | ✅ | ❌ |
+| [DPT](model_doc/dpt) | ✅ | ❌ | ❌ |
+| [EfficientFormer](model_doc/efficientformer) | ✅ | ✅ | ❌ |
+| [EfficientNet](model_doc/efficientnet) | ✅ | ❌ | ❌ |
+| [ELECTRA](model_doc/electra) | ✅ | ✅ | ✅ |
+| [EnCodec](model_doc/encodec) | ✅ | ❌ | ❌ |
+| [Encoder decoder](model_doc/encoder-decoder) | ✅ | ✅ | ✅ |
+| [ERNIE](model_doc/ernie) | ✅ | ❌ | ❌ |
+| [ErnieM](model_doc/ernie_m) | ✅ | ❌ | ❌ |
+| [ESM](model_doc/esm) | ✅ | ✅ | ❌ |
+| [FairSeq Machine-Translation](model_doc/fsmt) | ✅ | ❌ | ❌ |
+| [Falcon](model_doc/falcon) | ✅ | ❌ | ❌ |
+| [FLAN-T5](model_doc/flan-t5) | ✅ | ✅ | ✅ |
+| [FLAN-UL2](model_doc/flan-ul2) | ✅ | ✅ | ✅ |
+| [FlauBERT](model_doc/flaubert) | ✅ | ✅ | ❌ |
+| [FLAVA](model_doc/flava) | ✅ | ❌ | ❌ |
+| [FNet](model_doc/fnet) | ✅ | ❌ | ❌ |
+| [FocalNet](model_doc/focalnet) | ✅ | ❌ | ❌ |
+| [Funnel Transformer](model_doc/funnel) | ✅ | ✅ | ❌ |
+| [Fuyu](model_doc/fuyu) | ✅ | ❌ | ❌ |
+| [GIT](model_doc/git) | ✅ | ❌ | ❌ |
+| [GLPN](model_doc/glpn) | ✅ | ❌ | ❌ |
+| [GPT Neo](model_doc/gpt_neo) | ✅ | ❌ | ✅ |
+| [GPT NeoX](model_doc/gpt_neox) | ✅ | ❌ | ❌ |
+| [GPT NeoX Japanese](model_doc/gpt_neox_japanese) | ✅ | ❌ | ❌ |
+| [GPT-J](model_doc/gptj) | ✅ | ✅ | ✅ |
+| [GPT-Sw3](model_doc/gpt-sw3) | ✅ | ✅ | ✅ |
+| [GPTBigCode](model_doc/gpt_bigcode) | ✅ | ❌ | ❌ |
+| [GPTSAN-japanese](model_doc/gptsan-japanese) | ✅ | ❌ | ❌ |
+| [Graphormer](model_doc/graphormer) | ✅ | ❌ | ❌ |
+| [GroupViT](model_doc/groupvit) | ✅ | ✅ | ❌ |
+| [HerBERT](model_doc/herbert) | ✅ | ✅ | ✅ |
+| [Hubert](model_doc/hubert) | ✅ | ✅ | ❌ |
+| [I-BERT](model_doc/ibert) | ✅ | ❌ | ❌ |
+| [IDEFICS](model_doc/idefics) | ✅ | ❌ | ❌ |
+| [ImageGPT](model_doc/imagegpt) | ✅ | ❌ | ❌ |
+| [Informer](model_doc/informer) | ✅ | ❌ | ❌ |
+| [InstructBLIP](model_doc/instructblip) | ✅ | ❌ | ❌ |
+| [Jukebox](model_doc/jukebox) | ✅ | ❌ | ❌ |
+| [LayoutLM](model_doc/layoutlm) | ✅ | ✅ | ❌ |
+| [LayoutLMv2](model_doc/layoutlmv2) | ✅ | ❌ | ❌ |
+| [LayoutLMv3](model_doc/layoutlmv3) | ✅ | ✅ | ❌ |
+| [LayoutXLM](model_doc/layoutxlm) | ✅ | ❌ | ❌ |
+| [LED](model_doc/led) | ✅ | ✅ | ❌ |
+| [LeViT](model_doc/levit) | ✅ | ❌ | ❌ |
+| [LiLT](model_doc/lilt) | ✅ | ❌ | ❌ |
+| [LLaMA](model_doc/llama) | ✅ | ❌ | ❌ |
+| [Llama2](model_doc/llama2) | ✅ | ❌ | ❌ |
+| [Longformer](model_doc/longformer) | ✅ | ✅ | ❌ |
+| [LongT5](model_doc/longt5) | ✅ | ❌ | ✅ |
+| [LUKE](model_doc/luke) | ✅ | ❌ | ❌ |
+| [LXMERT](model_doc/lxmert) | ✅ | ✅ | ❌ |
+| [M-CTC-T](model_doc/mctct) | ✅ | ❌ | ❌ |
+| [M2M100](model_doc/m2m_100) | ✅ | ❌ | ❌ |
+| [Marian](model_doc/marian) | ✅ | ✅ | ✅ |
+| [MarkupLM](model_doc/markuplm) | ✅ | ❌ | ❌ |
+| [Mask2Former](model_doc/mask2former) | ✅ | ❌ | ❌ |
+| [MaskFormer](model_doc/maskformer) | ✅ | ❌ | ❌ |
+| [MatCha](model_doc/matcha) | ✅ | ❌ | ❌ |
+| [mBART](model_doc/mbart) | ✅ | ✅ | ✅ |
+| [mBART-50](model_doc/mbart50) | ✅ | ✅ | ✅ |
+| [MEGA](model_doc/mega) | ✅ | ❌ | ❌ |
+| [Megatron-BERT](model_doc/megatron-bert) | ✅ | ❌ | ❌ |
+| [Megatron-GPT2](model_doc/megatron_gpt2) | ✅ | ✅ | ✅ |
+| [MGP-STR](model_doc/mgp-str) | ✅ | ❌ | ❌ |
+| [Mistral](model_doc/mistral) | ✅ | ❌ | ❌ |
+| [mLUKE](model_doc/mluke) | ✅ | ❌ | ❌ |
+| [MMS](model_doc/mms) | ✅ | ✅ | ✅ |
+| [MobileBERT](model_doc/mobilebert) | ✅ | ✅ | ❌ |
+| [MobileNetV1](model_doc/mobilenet_v1) | ✅ | ❌ | ❌ |
+| [MobileNetV2](model_doc/mobilenet_v2) | ✅ | ❌ | ❌ |
+| [MobileViT](model_doc/mobilevit) | ✅ | ✅ | ❌ |
+| [MobileViTV2](model_doc/mobilevitv2) | ✅ | ❌ | ❌ |
+| [MPNet](model_doc/mpnet) | ✅ | ✅ | ❌ |
+| [MPT](model_doc/mpt) | ✅ | ❌ | ❌ |
+| [MRA](model_doc/mra) | ✅ | ❌ | ❌ |
+| [MT5](model_doc/mt5) | ✅ | ✅ | ✅ |
+| [MusicGen](model_doc/musicgen) | ✅ | ❌ | ❌ |
+| [MVP](model_doc/mvp) | ✅ | ❌ | ❌ |
+| [NAT](model_doc/nat) | ✅ | ❌ | ❌ |
+| [Nezha](model_doc/nezha) | ✅ | ❌ | ❌ |
+| [NLLB](model_doc/nllb) | ✅ | ❌ | ❌ |
+| [NLLB-MOE](model_doc/nllb-moe) | ✅ | ❌ | ❌ |
+| [Nougat](model_doc/nougat) | ✅ | ✅ | ✅ |
+| [Nyströmformer](model_doc/nystromformer) | ✅ | ❌ | ❌ |
+| [OneFormer](model_doc/oneformer) | ✅ | ❌ | ❌ |
+| [OpenAI GPT](model_doc/openai-gpt) | ✅ | ✅ | ❌ |
+| [OpenAI GPT-2](model_doc/gpt2) | ✅ | ✅ | ✅ |
+| [OpenLlama](model_doc/open-llama) | ✅ | ❌ | ❌ |
+| [OPT](model_doc/opt) | ✅ | ✅ | ✅ |
+| [OWL-ViT](model_doc/owlvit) | ✅ | ❌ | ❌ |
+| [OWLv2](model_doc/owlv2) | ✅ | ❌ | ❌ |
+| [Pegasus](model_doc/pegasus) | ✅ | ✅ | ✅ |
+| [PEGASUS-X](model_doc/pegasus_x) | ✅ | ❌ | ❌ |
+| [Perceiver](model_doc/perceiver) | ✅ | ❌ | ❌ |
+| [Persimmon](model_doc/persimmon) | ✅ | ❌ | ❌ |
+| [PhoBERT](model_doc/phobert) | ✅ | ✅ | ✅ |
+| [Pix2Struct](model_doc/pix2struct) | ✅ | ❌ | ❌ |
+| [PLBart](model_doc/plbart) | ✅ | ❌ | ❌ |
+| [PoolFormer](model_doc/poolformer) | ✅ | ❌ | ❌ |
+| [Pop2Piano](model_doc/pop2piano) | ✅ | ❌ | ❌ |
+| [ProphetNet](model_doc/prophetnet) | ✅ | ❌ | ❌ |
+| [PVT](model_doc/pvt) | ✅ | ❌ | ❌ |
+| [QDQBert](model_doc/qdqbert) | ✅ | ❌ | ❌ |
+| [RAG](model_doc/rag) | ✅ | ✅ | ❌ |
+| [REALM](model_doc/realm) | ✅ | ❌ | ❌ |
+| [Reformer](model_doc/reformer) | ✅ | ❌ | ❌ |
+| [RegNet](model_doc/regnet) | ✅ | ✅ | ✅ |
+| [RemBERT](model_doc/rembert) | ✅ | ✅ | ❌ |
+| [ResNet](model_doc/resnet) | ✅ | ✅ | ✅ |
+| [RetriBERT](model_doc/retribert) | ✅ | ❌ | ❌ |
+| [RoBERTa](model_doc/roberta) | ✅ | ✅ | ✅ |
+| [RoBERTa-PreLayerNorm](model_doc/roberta-prelayernorm) | ✅ | ✅ | ✅ |
+| [RoCBert](model_doc/roc_bert) | ✅ | ❌ | ❌ |
+| [RoFormer](model_doc/roformer) | ✅ | ✅ | ✅ |
+| [RWKV](model_doc/rwkv) | ✅ | ❌ | ❌ |
+| [SAM](model_doc/sam) | ✅ | ✅ | ❌ |
+| [SeamlessM4T](model_doc/seamless_m4t) | ✅ | ❌ | ❌ |
+| [SegFormer](model_doc/segformer) | ✅ | ✅ | ❌ |
+| [SEW](model_doc/sew) | ✅ | ❌ | ❌ |
+| [SEW-D](model_doc/sew-d) | ✅ | ❌ | ❌ |
+| [Speech Encoder decoder](model_doc/speech-encoder-decoder) | ✅ | ❌ | ✅ |
+| [Speech2Text](model_doc/speech_to_text) | ✅ | ✅ | ❌ |
+| [SpeechT5](model_doc/speecht5) | ✅ | ❌ | ❌ |
+| [Splinter](model_doc/splinter) | ✅ | ❌ | ❌ |
+| [SqueezeBERT](model_doc/squeezebert) | ✅ | ❌ | ❌ |
+| [SwiftFormer](model_doc/swiftformer) | ✅ | ❌ | ❌ |
+| [Swin Transformer](model_doc/swin) | ✅ | ✅ | ❌ |
+| [Swin Transformer V2](model_doc/swinv2) | ✅ | ❌ | ❌ |
+| [Swin2SR](model_doc/swin2sr) | ✅ | ❌ | ❌ |
+| [SwitchTransformers](model_doc/switch_transformers) | ✅ | ❌ | ❌ |
+| [T5](model_doc/t5) | ✅ | ✅ | ✅ |
+| [T5v1.1](model_doc/t5v1.1) | ✅ | ✅ | ✅ |
+| [Table Transformer](model_doc/table-transformer) | ✅ | ❌ | ❌ |
+| [TAPAS](model_doc/tapas) | ✅ | ✅ | ❌ |
+| [TAPEX](model_doc/tapex) | ✅ | ✅ | ✅ |
+| [Time Series Transformer](model_doc/time_series_transformer) | ✅ | ❌ | ❌ |
+| [TimeSformer](model_doc/timesformer) | ✅ | ❌ | ❌ |
+| [Trajectory Transformer](model_doc/trajectory_transformer) | ✅ | ❌ | ❌ |
+| [Transformer-XL](model_doc/transfo-xl) | ✅ | ✅ | ❌ |
+| [TrOCR](model_doc/trocr) | ✅ | ❌ | ❌ |
+| [TVLT](model_doc/tvlt) | ✅ | ❌ | ❌ |
+| [UL2](model_doc/ul2) | ✅ | ✅ | ✅ |
+| [UMT5](model_doc/umt5) | ✅ | ❌ | ❌ |
+| [UniSpeech](model_doc/unispeech) | ✅ | ❌ | ❌ |
+| [UniSpeechSat](model_doc/unispeech-sat) | ✅ | ❌ | ❌ |
+| [UPerNet](model_doc/upernet) | ✅ | ❌ | ❌ |
+| [VAN](model_doc/van) | ✅ | ❌ | ❌ |
+| [VideoMAE](model_doc/videomae) | ✅ | ❌ | ❌ |
+| [ViLT](model_doc/vilt) | ✅ | ❌ | ❌ |
+| [Vision Encoder decoder](model_doc/vision-encoder-decoder) | ✅ | ✅ | ✅ |
+| [VisionTextDualEncoder](model_doc/vision-text-dual-encoder) | ✅ | ✅ | ✅ |
+| [VisualBERT](model_doc/visual_bert) | ✅ | ❌ | ❌ |
+| [ViT](model_doc/vit) | ✅ | ✅ | ✅ |
+| [ViT Hybrid](model_doc/vit_hybrid) | ✅ | ❌ | ❌ |
+| [VitDet](model_doc/vitdet) | ✅ | ❌ | ❌ |
+| [ViTMAE](model_doc/vit_mae) | ✅ | ✅ | ❌ |
+| [ViTMatte](model_doc/vitmatte) | ✅ | ❌ | ❌ |
+| [ViTMSN](model_doc/vit_msn) | ✅ | ❌ | ❌ |
+| [VITS](model_doc/vits) | ✅ | ❌ | ❌ |
+| [ViViT](model_doc/vivit) | ✅ | ❌ | ❌ |
+| [Wav2Vec2](model_doc/wav2vec2) | ✅ | ✅ | ✅ |
+| [Wav2Vec2-Conformer](model_doc/wav2vec2-conformer) | ✅ | ❌ | ❌ |
+| [Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme) | ✅ | ✅ | ✅ |
+| [WavLM](model_doc/wavlm) | ✅ | ❌ | ❌ |
+| [Whisper](model_doc/whisper) | ✅ | ✅ | ✅ |
+| [X-CLIP](model_doc/xclip) | ✅ | ❌ | ❌ |
+| [X-MOD](model_doc/xmod) | ✅ | ❌ | ❌ |
+| [XGLM](model_doc/xglm) | ✅ | ✅ | ✅ |
+| [XLM](model_doc/xlm) | ✅ | ✅ | ❌ |
+| [XLM-ProphetNet](model_doc/xlm-prophetnet) | ✅ | ❌ | ❌ |
+| [XLM-RoBERTa](model_doc/xlm-roberta) | ✅ | ✅ | ✅ |
+| [XLM-RoBERTa-XL](model_doc/xlm-roberta-xl) | ✅ | ❌ | ❌ |
+| [XLM-V](model_doc/xlm-v) | ✅ | ✅ | ✅ |
+| [XLNet](model_doc/xlnet) | ✅ | ✅ | ❌ |
+| [XLS-R](model_doc/xls_r) | ✅ | ✅ | ✅ |
+| [XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2) | ✅ | ✅ | ✅ |
+| [YOLOS](model_doc/yolos) | ✅ | ❌ | ❌ |
+| [YOSO](model_doc/yoso) | ✅ | ❌ | ❌ |
+
+
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/_toctree.yml b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/_toctree.yml
new file mode 100644
index 0000000000000000000000000000000000000000..50123200bcb7b4ae0c6ebb5fbaf1f4c0c5e62b84
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/_toctree.yml
@@ -0,0 +1,164 @@
+- sections:
+ - local: index
+ title: 🤗 Transformers 简介
+ - local: quicktour
+ title: 快速上手
+ - local: installation
+ title: 安装
+ title: 开始使用
+- sections:
+ - local: pipeline_tutorial
+ title: 使用pipelines进行推理
+ - local: autoclass_tutorial
+ title: 使用AutoClass编写可移植的代码
+ - local: preprocessing
+ title: 预处理数据
+ - local: training
+ title: 微调预训练模型
+ - local: run_scripts
+ title: 通过脚本训练模型
+ - local: accelerate
+ title: 使用🤗Accelerate进行分布式训练
+ - local: peft
+ title: 使用🤗 PEFT加载和训练adapters
+ - local: model_sharing
+ title: 分享您的模型
+ - local: llm_tutorial
+ title: 使用LLMs进行生成
+ title: 教程
+- sections:
+ - isExpanded: false
+ sections:
+ - local: tasks/asr
+ title: 自动语音识别
+- sections:
+ - local: fast_tokenizers
+ title: 使用 🤗 Tokenizers 中的分词器
+ - local: multilingual
+ title: 使用多语言模型进行推理
+ - local: create_a_model
+ title: 使用特定于模型的 API
+ - local: custom_models
+ title: 共享自定义模型
+ - local: chat_templating
+ title: 聊天模型的模板
+ - local: serialization
+ title: 导出为 ONNX
+ - local: tflite
+ title: 导出为 TFLite
+ - local: torchscript
+ title: 导出为 TorchScript
+ - local: gguf
+ title: 与 GGUF 格式的互操作性
+ - local: tiktoken
+ title: 与 Tiktoken 文件的互操作性
+ - local: community
+ title: 社区资源
+ title: 开发者指南
+- sections:
+ - local: performance
+ title: 综述
+ - sections:
+ - local: fsdp
+ title: 完全分片数据并行
+ - local: perf_train_special
+ title: 在 Apple silicon 芯片上进行 PyTorch 训练
+ - local: perf_infer_gpu_multi
+ title: 多GPU推理
+ - local: perf_train_cpu
+ title: 在CPU上进行高效训练
+ - local: perf_hardware
+ title: 用于训练的定制硬件
+ - local: hpo_train
+ title: 使用Trainer API 进行超参数搜索
+ title: 高效训练技术
+ - local: big_models
+ title: 实例化大模型
+ - local: debugging
+ title: 问题定位及解决
+ - local: tf_xla
+ title: TensorFlow模型的XLA集成
+ - local: perf_torch_compile
+ title: 使用 `torch.compile()` 优化推理
+ title: 性能和可扩展性
+- sections:
+ - local: contributing
+ title: 如何为 🤗 Transformers 做贡献？
+ - local: add_new_pipeline
+ title: 如何将流水线添加到 🤗 Transformers？
+ title: 贡献
+- sections:
+ - local: philosophy
+ title: Transformers的设计理念
+ - local: task_summary
+ title: 🤗Transformers能做什么
+ - local: tokenizer_summary
+ title: 分词器的摘要
+ - local: attention
+ title: 注意力机制
+ - local: bertology
+ title: 基于BERT进行的相关研究
+ title: 概念指南
+- sections:
+ - sections:
+ - local: main_classes/callback
+ title: Callbacks
+ - local: main_classes/configuration
+ title: Configuration
+ - local: main_classes/data_collator
+ title: Data Collator
+ - local: main_classes/keras_callbacks
+ title: Keras callbacks
+ - local: main_classes/logging
+ title: Logging
+ - local: main_classes/model
+ title: 模型
+ - local: main_classes/text_generation
+ title: 文本生成
+ - local: main_classes/onnx
+ title: ONNX
+ - local: main_classes/optimizer_schedules
+ title: Optimization
+ - local: main_classes/output
+ title: 模型输出
+ - local: main_classes/pipelines
+ title: Pipelines
+ - local: main_classes/processors
+ title: Processors
+ - local: main_classes/quantization
+ title: Quantization
+ - local: main_classes/tokenizer
+ title: Tokenizer
+ - local: main_classes/trainer
+ title: Trainer
+ - local: main_classes/deepspeed
+ title: DeepSpeed集成
+ - local: main_classes/feature_extractor
+ title: Feature Extractor
+ - local: main_classes/image_processor
+ title: Image Processor
+ title: 主要类
+ - sections:
+ - local: internal/modeling_utils
+ title: 自定义层和工具
+ - local: internal/pipelines_utils
+ title: pipelines工具
+ - local: internal/tokenization_utils
+ title: Tokenizers工具
+ - local: internal/trainer_utils
+ title: 训练器工具
+ - local: internal/generation_utils
+ title: 生成工具
+ - local: internal/image_processing_utils
+ title: 图像处理工具
+ - local: internal/audio_utils
+ title: 音频处理工具
+ - local: internal/file_utils
+ title: 通用工具
+ - local: internal/time_series_utils
+ title: 时序数据工具
+ - sections:
+ - local: model_doc/bert
+ title: BERT
+ title: 内部辅助工具
+ title: 应用程序接口 (API)
\ No newline at end of file
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/accelerate.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/accelerate.md
new file mode 100644
index 0000000000000000000000000000000000000000..12c626199477dd14cfc322f642909132832cf258
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/accelerate.md
@@ -0,0 +1,132 @@
+
+
+# 🤗 加速分布式训练
+
+随着模型变得越来越大，并行性已经成为在有限硬件上训练更大模型和加速训练速度的策略，增加了数个数量级。在Hugging Face，我们创建了[🤗 加速](https://huggingface.co/docs/accelerate)库，以帮助用户在任何类型的分布式设置上轻松训练🤗 Transformers模型，无论是在一台机器上的多个GPU还是在多个机器上的多个GPU。在本教程中，了解如何自定义您的原生PyTorch训练循环，以启用分布式环境中的训练。
+
+## 设置
+
+通过安装🤗 加速开始:
+
+```bash
+pip install accelerate
+```
+
+然后导入并创建[`~accelerate.Accelerator`]对象。[`~accelerate.Accelerator`]将自动检测您的分布式设置类型，并初始化所有必要的训练组件。您不需要显式地将模型放在设备上。
+
+```py
+>>> from accelerate import Accelerator
+
+>>> accelerator = Accelerator()
+```
+
+## 准备加速
+
+下一步是将所有相关的训练对象传递给[`~accelerate.Accelerator.prepare`]方法。这包括您的训练和评估DataLoader、一个模型和一个优化器:
+
+```py
+>>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
+... train_dataloader, eval_dataloader, model, optimizer
+... )
+```
+
+## 反向传播
+
+最后一步是用🤗 加速的[`~accelerate.Accelerator.backward`]方法替换训练循环中的典型`loss.backward()`:
+
+```py
+>>> for epoch in range(num_epochs):
+... for batch in train_dataloader:
+... outputs = model(**batch)
+... loss = outputs.loss
+... accelerator.backward(loss)
+
+... optimizer.step()
+... lr_scheduler.step()
+... optimizer.zero_grad()
+... progress_bar.update(1)
+```
+
+如您在下面的代码中所见，您只需要添加四行额外的代码到您的训练循环中即可启用分布式训练！
+
+```diff
++ from accelerate import Accelerator
+ from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
+
++ accelerator = Accelerator()
+
+ model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
+ optimizer = AdamW(model.parameters(), lr=3e-5)
+
+- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+- model.to(device)
+
++ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
++ train_dataloader, eval_dataloader, model, optimizer
++ )
+
+ num_epochs = 3
+ num_training_steps = num_epochs * len(train_dataloader)
+ lr_scheduler = get_scheduler(
+ "linear",
+ optimizer=optimizer,
+ num_warmup_steps=0,
+ num_training_steps=num_training_steps
+ )
+
+ progress_bar = tqdm(range(num_training_steps))
+
+ model.train()
+ for epoch in range(num_epochs):
+ for batch in train_dataloader:
+- batch = {k: v.to(device) for k, v in batch.items()}
+ outputs = model(**batch)
+ loss = outputs.loss
+- loss.backward()
++ accelerator.backward(loss)
+
+ optimizer.step()
+ lr_scheduler.step()
+ optimizer.zero_grad()
+ progress_bar.update(1)
+```
+
+## 训练
+
+在添加了相关代码行后，可以在脚本或笔记本（如Colaboratory）中启动训练。
+
+### 用脚本训练
+
+如果您从脚本中运行训练，请运行以下命令以创建和保存配置文件:
+
+```bash
+accelerate config
+```
+
+然后使用以下命令启动训练:
+
+```bash
+accelerate launch train.py
+```
+
+### 用笔记本训练
+
+🤗 加速还可以在笔记本中运行，如果您计划使用Colaboratory的TPU，则可在其中运行。将负责训练的所有代码包装在一个函数中，并将其传递给[`~accelerate.notebook_launcher`]:
+
+```py
+>>> from accelerate import notebook_launcher
+
+>>> notebook_launcher(training_function)
+```
+
+有关🤗 加速及其丰富功能的更多信息，请参阅[文档](https://huggingface.co/docs/accelerate)。
\ No newline at end of file
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/add_new_pipeline.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/add_new_pipeline.md
new file mode 100644
index 0000000000000000000000000000000000000000..57fd53636b0a1360f44ecf9af9f94c37d1e84f45
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/add_new_pipeline.md
@@ -0,0 +1,238 @@
+
+
+# 如何创建自定义流水线？
+
+在本指南中，我们将演示如何创建一个自定义流水线并分享到 [Hub](https://hf.co/models)，或将其添加到 🤗 Transformers 库中。
+
+首先，你需要决定流水线将能够接受的原始条目。它可以是字符串、原始字节、字典或任何看起来最可能是期望的输入。
+尽量保持输入为纯 Python 语言，因为这样可以更容易地实现兼容性（甚至通过 JSON 在其他语言之间）。
+这些将是流水线 (`preprocess`) 的 `inputs`。
+
+然后定义 `outputs`。与 `inputs` 相同的策略。越简单越好。这些将是 `postprocess` 方法的输出。
+
+首先继承基类 `Pipeline`，其中包含实现 `preprocess`、`_forward`、`postprocess` 和 `_sanitize_parameters` 所需的 4 个方法。
+
+```python
+from transformers import Pipeline
+
+
+class MyPipeline(Pipeline):
+ def _sanitize_parameters(self, **kwargs):
+ preprocess_kwargs = {}
+ if "maybe_arg" in kwargs:
+ preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
+ return preprocess_kwargs, {}, {}
+
+ def preprocess(self, inputs, maybe_arg=2):
+ model_input = Tensor(inputs["input_ids"])
+ return {"model_input": model_input}
+
+ def _forward(self, model_inputs):
+ # model_inputs == {"model_input": model_input}
+ outputs = self.model(**model_inputs)
+ # Maybe {"logits": Tensor(...)}
+ return outputs
+
+ def postprocess(self, model_outputs):
+ best_class = model_outputs["logits"].softmax(-1)
+ return best_class
+```
+
+这种分解的结构旨在为 CPU/GPU 提供相对无缝的支持，同时支持在不同线程上对 CPU 进行预处理/后处理。
+
+`preprocess` 将接受最初定义的输入，并将其转换为可供模型输入的内容。它可能包含更多信息，通常是一个 `Dict`。
+
+`_forward` 是实现细节，不应直接调用。`forward` 是首选的调用方法，因为它包含保障措施，以确保一切都在预期的设备上运作。
+如果任何内容与实际模型相关，它应该属于 `_forward` 方法，其他内容应该在 preprocess/postprocess 中。
+
+`postprocess` 方法将接受 `_forward` 的输出，并将其转换为之前确定的最终输出。
+
+`_sanitize_parameters` 存在是为了允许用户在任何时候传递任何参数，无论是在初始化时 `pipeline(...., maybe_arg=4)`
+还是在调用时 `pipe = pipeline(...); output = pipe(...., maybe_arg=4)`。
+
+`_sanitize_parameters` 的返回值是将直接传递给 `preprocess`、`_forward` 和 `postprocess` 的 3 个关键字参数字典。
+如果调用方没有使用任何额外参数调用，则不要填写任何内容。这样可以保留函数定义中的默认参数，这总是更"自然"的。
+
+在分类任务中，一个经典的例子是在后处理中使用 `top_k` 参数。
+
+```python
+>>> pipe = pipeline("my-new-task")
+>>> pipe("This is a test")
+[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}, {"label": "3-star", "score": 0.05}
+{"label": "4-star", "score": 0.025}, {"label": "5-star", "score": 0.025}]
+
+>>> pipe("This is a test", top_k=2)
+[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}]
+```
+
+为了实现这一点，我们将更新我们的 `postprocess` 方法，将默认参数设置为 `5`，
+并编辑 `_sanitize_parameters` 方法，以允许这个新参数。
+
+```python
+def postprocess(self, model_outputs, top_k=5):
+ best_class = model_outputs["logits"].softmax(-1)
+ # Add logic to handle top_k
+ return best_class
+
+
+def _sanitize_parameters(self, **kwargs):
+ preprocess_kwargs = {}
+ if "maybe_arg" in kwargs:
+ preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
+
+ postprocess_kwargs = {}
+ if "top_k" in kwargs:
+ postprocess_kwargs["top_k"] = kwargs["top_k"]
+ return preprocess_kwargs, {}, postprocess_kwargs
+```
+
+尽量保持简单输入/输出，最好是可 JSON 序列化的，因为这样可以使流水线的使用非常简单，而不需要用户了解新的对象类型。
+通常也相对常见地支持许多不同类型的参数以便使用（例如音频文件，可以是文件名、URL 或纯字节）。
+
+## 将其添加到支持的任务列表中
+
+要将你的 `new-task` 注册到支持的任务列表中，你需要将其添加到 `PIPELINE_REGISTRY` 中：
+
+```python
+from transformers.pipelines import PIPELINE_REGISTRY
+
+PIPELINE_REGISTRY.register_pipeline(
+ "new-task",
+ pipeline_class=MyPipeline,
+ pt_model=AutoModelForSequenceClassification,
+)
+```
+
+如果需要，你可以指定一个默认模型，此时它应该带有一个特定的修订版本（可以是分支名称或提交哈希，这里我们使用了 `"abcdef"`），以及类型：
+
+```python
+PIPELINE_REGISTRY.register_pipeline(
+ "new-task",
+ pipeline_class=MyPipeline,
+ pt_model=AutoModelForSequenceClassification,
+ default={"pt": ("user/awesome_model", "abcdef")},
+ type="text", # current support type: text, audio, image, multimodal
+)
+```
+
+## 在 Hub 上分享你的流水线
+
+要在 Hub 上分享你的自定义流水线，你只需要将 `Pipeline` 子类的自定义代码保存在一个 Python 文件中。
+例如，假设我们想使用一个自定义流水线进行句对分类，如下所示：
+
+```py
+import numpy as np
+
+from transformers import Pipeline
+
+
+def softmax(outputs):
+ maxes = np.max(outputs, axis=-1, keepdims=True)
+ shifted_exp = np.exp(outputs - maxes)
+ return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
+
+
+class PairClassificationPipeline(Pipeline):
+ def _sanitize_parameters(self, **kwargs):
+ preprocess_kwargs = {}
+ if "second_text" in kwargs:
+ preprocess_kwargs["second_text"] = kwargs["second_text"]
+ return preprocess_kwargs, {}, {}
+
+ def preprocess(self, text, second_text=None):
+ return self.tokenizer(text, text_pair=second_text, return_tensors=self.framework)
+
+ def _forward(self, model_inputs):
+ return self.model(**model_inputs)
+
+ def postprocess(self, model_outputs):
+ logits = model_outputs.logits[0].numpy()
+ probabilities = softmax(logits)
+
+ best_class = np.argmax(probabilities)
+ label = self.model.config.id2label[best_class]
+ score = probabilities[best_class].item()
+ logits = logits.tolist()
+ return {"label": label, "score": score, "logits": logits}
+```
+
+这个实现与框架无关，适用于 PyTorch 和 TensorFlow 模型。如果我们将其保存在一个名为
+`pair_classification.py` 的文件中，然后我们可以像这样导入并注册它：
+
+```py
+from pair_classification import PairClassificationPipeline
+from transformers.pipelines import PIPELINE_REGISTRY
+from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
+
+PIPELINE_REGISTRY.register_pipeline(
+ "pair-classification",
+ pipeline_class=PairClassificationPipeline,
+ pt_model=AutoModelForSequenceClassification,
+ tf_model=TFAutoModelForSequenceClassification,
+)
+```
+
+完成这些步骤后，我们可以将其与预训练模型一起使用。例如，`sgugger/finetuned-bert-mrpc`
+已经在 MRPC 数据集上进行了微调，用于将句子对分类为是释义或不是释义。
+
+```py
+from transformers import pipeline
+
+classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
+```
+
+然后，我们可以通过在 `Repository` 中使用 `save_pretrained` 方法将其分享到 Hub 上：
+
+```py
+from huggingface_hub import Repository
+
+repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline")
+classifier.save_pretrained("test-dynamic-pipeline")
+repo.push_to_hub()
+```
+
+这将会复制包含你定义的 `PairClassificationPipeline` 的文件到文件夹 `"test-dynamic-pipeline"` 中，
+同时保存流水线的模型和分词器，然后将所有内容推送到仓库 `{your_username}/test-dynamic-pipeline` 中。
+之后，只要提供选项 `trust_remote_code=True`，任何人都可以使用它：
+
+```py
+from transformers import pipeline
+
+classifier = pipeline(model="{your_username}/test-dynamic-pipeline", trust_remote_code=True)
+```
+
+## 将流水线添加到 🤗 Transformers
+
+如果你想将你的流水线贡献给 🤗 Transformers，你需要在 `pipelines` 子模块中添加一个新模块，
+其中包含你的流水线的代码，然后将其添加到 `pipelines/__init__.py` 中定义的任务列表中。
+
+然后，你需要添加测试。创建一个新文件 `tests/test_pipelines_MY_PIPELINE.py`，其中包含其他测试的示例。
+
+`run_pipeline_test` 函数将非常通用，并在每种可能的架构上运行小型随机模型，如 `model_mapping` 和 `tf_model_mapping` 所定义。
+
+这对于测试未来的兼容性非常重要，这意味着如果有人为 `XXXForQuestionAnswering` 添加了一个新模型，
+流水线测试将尝试在其上运行。由于模型是随机的，所以不可能检查实际值，这就是为什么有一个帮助函数 `ANY`，它只是尝试匹配流水线的输出类型。
+
+你还 **需要** 实现 2（最好是 4）个测试。
+
+- `test_small_model_pt`：为这个流水线定义一个小型模型（结果是否合理并不重要），并测试流水线的输出。
+ 结果应该与 `test_small_model_tf` 的结果相同。
+- `test_small_model_tf`：为这个流水线定义一个小型模型（结果是否合理并不重要），并测试流水线的输出。
+ 结果应该与 `test_small_model_pt` 的结果相同。
+- `test_large_model_pt`（可选）：在一个真实的流水线上测试流水线，结果应该是有意义的。
+ 这些测试速度较慢，应该被如此标记。这里的目标是展示流水线，并确保在未来的发布中没有漂移。
+- `test_large_model_tf`（可选）：在一个真实的流水线上测试流水线，结果应该是有意义的。
+ 这些测试速度较慢，应该被如此标记。这里的目标是展示流水线，并确保在未来的发布中没有漂移。
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/attention.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/attention.md
new file mode 100644
index 0000000000000000000000000000000000000000..357a574a2d2e697f2c5b5254e21cb5037521587f
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/attention.md
@@ -0,0 +1,37 @@
+
+
+# 注意力机制
+
+大多数 transformer 模型使用完全注意力机制，该机制采用正方形的注意力矩阵。当输入很长的文本时，这将导致巨大的计算瓶颈。Longformer 和 Reformer 是提高注意力机制效率的改进模型，它们使用稀疏化的注意力矩阵来加速训练。
+
+## 局部敏感哈希注意力机制（LSH attention）
+
+[Reformer](model_doc/reformer)使用LSH（局部敏感哈希）的注意力机制。在计算softmax(QK^t)时，只有矩阵QK^t中的最大元素（在softmax维度上）会做出有用的贡献。所以对于Q中的每个查询q，我们只需要考虑K中与q接近的键k，这里使用了一个哈希函数来确定q和k是否接近。注意力掩码被修改以掩盖当前的词符（token）（除了第一个位置之外），因为这样会使得查询和键相等（因此非常相似）。由于哈希可能会有些随机性，所以在实践中使用多个哈希函数（由n_rounds参数确定）,然后一起求平均。
+
+## 局部注意力机制（Local attention）
+[Longformer](model_doc/longformer)使用局部注意力机制:通常情况下，局部上下文（例如，左边和右边的两个词符是什么？）对于给定词符的操作已经足够了。此外，通过堆叠具有小窗口的注意力层，最后一层将拥有不仅仅是窗口内词符的感受野，这使得它们能构建整个句子的表示。
+
+一些预先选定的输入词符也被赋予全局注意力:对于这些少数词符，注意力矩阵可以访问所有词符（tokens），并且这个过程是对称的:所有其他词符除了它们局部窗口内的词符之外，也可以访问这些特定的词符。这在论文的图2d中有展示，下面是一个样本注意力掩码：
+
+
+ 
+
+
+使用参数更少的注意力矩阵，可以让模型处理更长的输入序列。
+
+## 其他技巧
+
+### 轴向位置编码
+
+[Reformer](model_doc/reformer)模型使用轴向位置编码：在传统的transformer模型中，位置编码矩阵E的大小是\\(l\\)乘以\\(d\\)，其中\\(l\\)是序列长度，\\(d\\)是隐藏状态的维度。如果你有非常长的文本，这个矩阵可能会非常大，将会占用大量的GPU显存。为了缓解这个问题，轴向位置编码将这个大矩阵E分解成两个较小的矩阵E1和E2，它们的维度分别是\\(l_{1} \times d_{1}\\) 和\\(l_{2} \times d_{2}\\)，满足\\(l_{1} \times l_{2} = l\\)和\\(d_{1} + d_{2} = d\\)（通过长度的乘积，最终得到的矩阵要小得多）。在E中，对于时间步\\(j\\) 的嵌入是通过连接E1中时间步 \\(j \% l1\\) 的嵌入和E2中时间步\\(j // l1\\)的嵌入来获得的。
+
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/autoclass_tutorial.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/autoclass_tutorial.md
new file mode 100644
index 0000000000000000000000000000000000000000..f056f12d787be1f5a51b06df844467d8ea7b5f94
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/autoclass_tutorial.md
@@ -0,0 +1,149 @@
+
+
+# 使用AutoClass加载预训练实例
+
+由于存在许多不同的Transformer架构，因此为您的checkpoint创建一个可用架构可能会具有挑战性。通过`AutoClass`可以自动推断并从给定的checkpoint加载正确的架构, 这也是🤗 Transformers易于使用、简单且灵活核心规则的重要一部分。`from_pretrained()`方法允许您快速加载任何架构的预训练模型，因此您不必花费时间和精力从头开始训练模型。生成这种与checkpoint无关的代码意味着，如果您的代码适用于一个checkpoint，它将适用于另一个checkpoint - 只要它们是为了类似的任务进行训练的 - 即使架构不同。
+
+
++☐ 如果你的 Pull request 解决了一个issue，请在 Pull request 描述中提及该 issue 的编号，以确保它们被关联起来（这样查看 issue 的人就知道你正在处理它）。
+☐ 如果是正在进行中的工作，请在标题前加上 [WIP]。这有助于避免重复工作和区分哪些 PR 可以合并。
+☐ 确保可以通过现有的测试。
+☐ 如果添加了新功能，请同时添加对应的测试。
+ - 如果添加一个新模型，请使用 `ModelTester.all_model_classes = (MyModel, MyModelWithLMHead,...)` 来触发通用测试。 + - 如果你正在添加新的 `@slow` 测试，请确保通过以下检查：`RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py` + - 如果你正在添加一个新的分词器，请编写测试并确保通过以下检查：`RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` + - CircleCI 不会运行时间较长的测试，但 GitHub Actions 每晚会运行所有测试！
+ +☐ 所有公共 method 必须具有信息文档（比如 [`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py)）。
+☐ 由于代码仓库的体积正在迅速增长，请避免添加图像、视频和其他非文本文件，它们会增加仓库的负担。请使用 [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) 等 Hub 仓库来托管这些文件，并通过 URL 引用它们。我们建议将与文档相关的图片放置在以下仓库中：[huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images)。你可以在这个数据集仓库上创建一个 PR，并请求 Hugging Face 成员进行合并。 + +要了解更多有关在 Pull request 上运行的检查的信息，请查看我们的 [检查 Pull Request](https://huggingface.co/docs/transformers/pr_checks) 指南。 + +### 测试 + +包含了广泛的测试套件来测试库的行为和一些示例。库测试可以在 [tests](https://github.com/huggingface/transformers/tree/main/tests) 文件夹中找到，示例测试可以在 [examples](https://github.com/huggingface/transformers/tree/main/examples) 文件夹中找到。 + +我们喜欢使用 `pytest` 和 `pytest-xdist`，因为它运行更快。在仓库的根目录，指定一个*子文件夹的路径或测试文件*来运行测试： + +```bash +python -m pytest -n auto --dist=loadfile -s -v ./tests/models/my_new_model +``` + +同样地，在 `examples` 目录，指定一个*子文件夹的路径或测试文件* 来运行测试。例如，以下命令会测试 PyTorch `examples` 目录中的文本分类子文件夹： + +```bash +pip install -r examples/xxx/requirements.txt # 仅在第一次需要 +python -m pytest -n auto --dist=loadfile -s -v ./examples/pytorch/text-classification +``` + +实际上这就是我们的 `make test` 和 `make test-examples` 命令的实现方式（不包括 `pip install`）！ + +你也可以指定一个较小的测试集来仅测试特定功能。 + +默认情况下，会跳过时间较长的测试，但你可以将 `RUN_SLOW` 环境变量设置为 `yes` 来运行它们。这将下载以 GB 为单位的模型文件，所以确保你有足够的磁盘空间、良好的网络连接和足够的耐心！ + +
+🖼️ **机器视觉**：图像分类、目标检测和语义分割。
+🗣️ **音频**：自动语音识别和音频分类。
+🐙 **多模态**：表格问答、光学字符识别、从扫描文档提取信息、视频分类和视觉问答。 + +🤗 Transformers 支持在 PyTorch、TensorFlow 和 JAX 上的互操作性. 这给在模型的每个阶段使用不同的框架带来了灵活性；在一个框架中使用几行代码训练一个模型，然后在另一个框架中加载它并进行推理。模型也可以被导出为 ONNX 和 TorchScript 格式，用于在生产环境中部署。 + +马上加入在 [Hub](https://huggingface.co/models)、[论坛](https://discuss.huggingface.co/) 或者 [Discord](https://discord.com/invite/JfAtkvEtRb) 上正在快速发展的社区吧！ + +## 如果你需要来自 Hugging Face 团队的个性化支持 + + +
+
+
+## 目录
+
+这篇文档由以下 5 个章节组成：
+
+- **开始使用** 包含了库的快速上手和安装说明，便于配置和运行。
+- **教程** 是一个初学者开始的好地方。本章节将帮助你获得你会用到的使用这个库的基本技能。
+- **操作指南** 向你展示如何实现一个特定目标，比如为语言建模微调一个预训练模型或者如何创造并分享个性化模型。
+- **概念指南** 对 🤗 Transformers 的模型，任务和设计理念背后的基本概念和思想做了更多的讨论和解释。
+- **API 介绍** 描述了所有的类和函数：
+
+ - **主要类别** 详述了配置（configuration）、模型（model）、分词器（tokenizer）和流水线（pipeline）这几个最重要的类。
+ - **模型** 详述了在这个库中和每个模型实现有关的类和函数。
+ - **内部帮助** 详述了内部使用的工具类和函数。
+
+### 支持的模型和框架
+
+下表展示了库中对每个模型的支持情况，如是否具有 Python 分词器（表中的“Tokenizer slow”）、是否具有由 🤗 Tokenizers 库支持的快速分词器（表中的“Tokenizer fast”）、是否支持 Jax（通过 Flax）、PyTorch 与 TensorFlow。
+
+
+
+| 模型 | PyTorch 支持 | TensorFlow 支持 | Flax 支持 |
+|:------------------------------------------------------------------------:|:---------------:|:------------------:|:------------:|
+| [ALBERT](../en/model_doc/albert) | ✅ | ✅ | ✅ |
+| [ALIGN](../en/model_doc/align) | ✅ | ❌ | ❌ |
+| [AltCLIP](../en/model_doc/altclip) | ✅ | ❌ | ❌ |
+| [Audio Spectrogram Transformer](../en/model_doc/audio-spectrogram-transformer) | ✅ | ❌ | ❌ |
+| [Autoformer](../en/model_doc/autoformer) | ✅ | ❌ | ❌ |
+| [Bark](../en/model_doc/bark) | ✅ | ❌ | ❌ |
+| [BART](../en/model_doc/bart) | ✅ | ✅ | ✅ |
+| [BARThez](../en/model_doc/barthez) | ✅ | ✅ | ✅ |
+| [BARTpho](../en/model_doc/bartpho) | ✅ | ✅ | ✅ |
+| [BEiT](../en/model_doc/beit) | ✅ | ❌ | ✅ |
+| [BERT](../en/model_doc/bert) | ✅ | ✅ | ✅ |
+| [Bert Generation](../en/model_doc/bert-generation) | ✅ | ❌ | ❌ |
+| [BertJapanese](../en/model_doc/bert-japanese) | ✅ | ✅ | ✅ |
+| [BERTweet](../en/model_doc/bertweet) | ✅ | ✅ | ✅ |
+| [BigBird](../en/model_doc/big_bird) | ✅ | ❌ | ✅ |
+| [BigBird-Pegasus](../en/model_doc/bigbird_pegasus) | ✅ | ❌ | ❌ |
+| [BioGpt](../en/model_doc/biogpt) | ✅ | ❌ | ❌ |
+| [BiT](../en/model_doc/bit) | ✅ | ❌ | ❌ |
+| [Blenderbot](../en/model_doc/blenderbot) | ✅ | ✅ | ✅ |
+| [BlenderbotSmall](../en/model_doc/blenderbot-small) | ✅ | ✅ | ✅ |
+| [BLIP](../en/model_doc/blip) | ✅ | ✅ | ❌ |
+| [BLIP-2](../en/model_doc/blip-2) | ✅ | ❌ | ❌ |
+| [BLOOM](../en/model_doc/bloom) | ✅ | ❌ | ✅ |
+| [BORT](../en/model_doc/bort) | ✅ | ✅ | ✅ |
+| [BridgeTower](../en/model_doc/bridgetower) | ✅ | ❌ | ❌ |
+| [BROS](../en/model_doc/bros) | ✅ | ❌ | ❌ |
+| [ByT5](../en/model_doc/byt5) | ✅ | ✅ | ✅ |
+| [CamemBERT](../en/model_doc/camembert) | ✅ | ✅ | ❌ |
+| [CANINE](../en/model_doc/canine) | ✅ | ❌ | ❌ |
+| [Chinese-CLIP](../en/model_doc/chinese_clip) | ✅ | ❌ | ❌ |
+| [CLAP](../en/model_doc/clap) | ✅ | ❌ | ❌ |
+| [CLIP](../en/model_doc/clip) | ✅ | ✅ | ✅ |
+| [CLIPSeg](../en/model_doc/clipseg) | ✅ | ❌ | ❌ |
+| [CLVP](../en/model_doc/clvp) | ✅ | ❌ | ❌ |
+| [CodeGen](../en/model_doc/codegen) | ✅ | ❌ | ❌ |
+| [CodeLlama](../en/model_doc/code_llama) | ✅ | ❌ | ✅ |
+| [Conditional DETR](../en/model_doc/conditional_detr) | ✅ | ❌ | ❌ |
+| [ConvBERT](../en/model_doc/convbert) | ✅ | ✅ | ❌ |
+| [ConvNeXT](../en/model_doc/convnext) | ✅ | ✅ | ❌ |
+| [ConvNeXTV2](../en/model_doc/convnextv2) | ✅ | ✅ | ❌ |
+| [CPM](../en/model_doc/cpm) | ✅ | ✅ | ✅ |
+| [CPM-Ant](../en/model_doc/cpmant) | ✅ | ❌ | ❌ |
+| [CTRL](../en/model_doc/ctrl) | ✅ | ✅ | ❌ |
+| [CvT](../en/model_doc/cvt) | ✅ | ✅ | ❌ |
+| [Data2VecAudio](../en/model_doc/data2vec) | ✅ | ❌ | ❌ |
+| [Data2VecText](../en/model_doc/data2vec) | ✅ | ❌ | ❌ |
+| [Data2VecVision](../en/model_doc/data2vec) | ✅ | ✅ | ❌ |
+| [DeBERTa](../en/model_doc/deberta) | ✅ | ✅ | ❌ |
+| [DeBERTa-v2](../en/model_doc/deberta-v2) | ✅ | ✅ | ❌ |
+| [Decision Transformer](../en/model_doc/decision_transformer) | ✅ | ❌ | ❌ |
+| [Deformable DETR](../en/model_doc/deformable_detr) | ✅ | ❌ | ❌ |
+| [DeiT](../en/model_doc/deit) | ✅ | ✅ | ❌ |
+| [DePlot](../en/model_doc/deplot) | ✅ | ❌ | ❌ |
+| [Depth Anything](../en/model_doc/depth_anything) | ✅ | ❌ | ❌ |
+| [DETA](../en/model_doc/deta) | ✅ | ❌ | ❌ |
+| [DETR](../en/model_doc/detr) | ✅ | ❌ | ❌ |
+| [DialoGPT](../en/model_doc/dialogpt) | ✅ | ✅ | ✅ |
+| [DiNAT](../en/model_doc/dinat) | ✅ | ❌ | ❌ |
+| [DINOv2](../en/model_doc/dinov2) | ✅ | ❌ | ❌ |
+| [DistilBERT](../en/model_doc/distilbert) | ✅ | ✅ | ✅ |
+| [DiT](../en/model_doc/dit) | ✅ | ❌ | ✅ |
+| [DonutSwin](../en/model_doc/donut) | ✅ | ❌ | ❌ |
+| [DPR](../en/model_doc/dpr) | ✅ | ✅ | ❌ |
+| [DPT](../en/model_doc/dpt) | ✅ | ❌ | ❌ |
+| [EfficientFormer](../en/model_doc/efficientformer) | ✅ | ✅ | ❌ |
+| [EfficientNet](../en/model_doc/efficientnet) | ✅ | ❌ | ❌ |
+| [ELECTRA](../en/model_doc/electra) | ✅ | ✅ | ✅ |
+| [EnCodec](../en/model_doc/encodec) | ✅ | ❌ | ❌ |
+| [Encoder decoder](../en/model_doc/encoder-decoder) | ✅ | ✅ | ✅ |
+| [ERNIE](../en/model_doc/ernie) | ✅ | ❌ | ❌ |
+| [ErnieM](../en/model_doc/ernie_m) | ✅ | ❌ | ❌ |
+| [ESM](../en/model_doc/esm) | ✅ | ✅ | ❌ |
+| [FairSeq Machine-Translation](../en/model_doc/fsmt) | ✅ | ❌ | ❌ |
+| [Falcon](../en/model_doc/falcon) | ✅ | ❌ | ❌ |
+| [FastSpeech2Conformer](../en/model_doc/fastspeech2_conformer) | ✅ | ❌ | ❌ |
+| [FLAN-T5](../en/model_doc/flan-t5) | ✅ | ✅ | ✅ |
+| [FLAN-UL2](../en/model_doc/flan-ul2) | ✅ | ✅ | ✅ |
+| [FlauBERT](../en/model_doc/flaubert) | ✅ | ✅ | ❌ |
+| [FLAVA](../en/model_doc/flava) | ✅ | ❌ | ❌ |
+| [FNet](../en/model_doc/fnet) | ✅ | ❌ | ❌ |
+| [FocalNet](../en/model_doc/focalnet) | ✅ | ❌ | ❌ |
+| [Funnel Transformer](../en/model_doc/funnel) | ✅ | ✅ | ❌ |
+| [Fuyu](../en/model_doc/fuyu) | ✅ | ❌ | ❌ |
+| [Gemma](../en/model_doc/gemma) | ✅ | ❌ | ✅ |
+| [GIT](../en/model_doc/git) | ✅ | ❌ | ❌ |
+| [GLPN](../en/model_doc/glpn) | ✅ | ❌ | ❌ |
+| [GPT Neo](../en/model_doc/gpt_neo) | ✅ | ❌ | ✅ |
+| [GPT NeoX](../en/model_doc/gpt_neox) | ✅ | ❌ | ❌ |
+| [GPT NeoX Japanese](../en/model_doc/gpt_neox_japanese) | ✅ | ❌ | ❌ |
+| [GPT-J](../en/model_doc/gptj) | ✅ | ✅ | ✅ |
+| [GPT-Sw3](../en/model_doc/gpt-sw3) | ✅ | ✅ | ✅ |
+| [GPTBigCode](../en/model_doc/gpt_bigcode) | ✅ | ❌ | ❌ |
+| [GPTSAN-japanese](../en/model_doc/gptsan-japanese) | ✅ | ❌ | ❌ |
+| [Graphormer](../en/model_doc/graphormer) | ✅ | ❌ | ❌ |
+| [GroupViT](../en/model_doc/groupvit) | ✅ | ✅ | ❌ |
+| [HerBERT](../en/model_doc/herbert) | ✅ | ✅ | ✅ |
+| [Hubert](../en/model_doc/hubert) | ✅ | ✅ | ❌ |
+| [I-BERT](../en/model_doc/ibert) | ✅ | ❌ | ❌ |
+| [IDEFICS](../en/model_doc/idefics) | ✅ | ❌ | ❌ |
+| [ImageGPT](../en/model_doc/imagegpt) | ✅ | ❌ | ❌ |
+| [Informer](../en/model_doc/informer) | ✅ | ❌ | ❌ |
+| [InstructBLIP](../en/model_doc/instructblip) | ✅ | ❌ | ❌ |
+| [Jukebox](../en/model_doc/jukebox) | ✅ | ❌ | ❌ |
+| [KOSMOS-2](../en/model_doc/kosmos-2) | ✅ | ❌ | ❌ |
+| [LayoutLM](../en/model_doc/layoutlm) | ✅ | ✅ | ❌ |
+| [LayoutLMv2](../en/model_doc/layoutlmv2) | ✅ | ❌ | ❌ |
+| [LayoutLMv3](../en/model_doc/layoutlmv3) | ✅ | ✅ | ❌ |
+| [LayoutXLM](../en/model_doc/layoutxlm) | ✅ | ❌ | ❌ |
+| [LED](../en/model_doc/led) | ✅ | ✅ | ❌ |
+| [LeViT](../en/model_doc/levit) | ✅ | ❌ | ❌ |
+| [LiLT](../en/model_doc/lilt) | ✅ | ❌ | ❌ |
+| [LLaMA](../en/model_doc/llama) | ✅ | ❌ | ✅ |
+| [Llama2](../en/model_doc/llama2) | ✅ | ❌ | ✅ |
+| [LLaVa](../en/model_doc/llava) | ✅ | ❌ | ❌ |
+| [Longformer](../en/model_doc/longformer) | ✅ | ✅ | ❌ |
+| [LongT5](../en/model_doc/longt5) | ✅ | ❌ | ✅ |
+| [LUKE](../en/model_doc/luke) | ✅ | ❌ | ❌ |
+| [LXMERT](../en/model_doc/lxmert) | ✅ | ✅ | ❌ |
+| [M-CTC-T](../en/model_doc/mctct) | ✅ | ❌ | ❌ |
+| [M2M100](../en/model_doc/m2m_100) | ✅ | ❌ | ❌ |
+| [MADLAD-400](../en/model_doc/madlad-400) | ✅ | ✅ | ✅ |
+| [Marian](../en/model_doc/marian) | ✅ | ✅ | ✅ |
+| [MarkupLM](../en/model_doc/markuplm) | ✅ | ❌ | ❌ |
+| [Mask2Former](../en/model_doc/mask2former) | ✅ | ❌ | ❌ |
+| [MaskFormer](../en/model_doc/maskformer) | ✅ | ❌ | ❌ |
+| [MatCha](../en/model_doc/matcha) | ✅ | ❌ | ❌ |
+| [mBART](../en/model_doc/mbart) | ✅ | ✅ | ✅ |
+| [mBART-50](../en/model_doc/mbart50) | ✅ | ✅ | ✅ |
+| [MEGA](../en/model_doc/mega) | ✅ | ❌ | ❌ |
+| [Megatron-BERT](../en/model_doc/megatron-bert) | ✅ | ❌ | ❌ |
+| [Megatron-GPT2](../en/model_doc/megatron_gpt2) | ✅ | ✅ | ✅ |
+| [MGP-STR](../en/model_doc/mgp-str) | ✅ | ❌ | ❌ |
+| [Mistral](../en/model_doc/mistral) | ✅ | ❌ | ✅ |
+| [Mixtral](../en/model_doc/mixtral) | ✅ | ❌ | ❌ |
+| [mLUKE](../en/model_doc/mluke) | ✅ | ❌ | ❌ |
+| [MMS](../en/model_doc/mms) | ✅ | ✅ | ✅ |
+| [MobileBERT](../en/model_doc/mobilebert) | ✅ | ✅ | ❌ |
+| [MobileNetV1](../en/model_doc/mobilenet_v1) | ✅ | ❌ | ❌ |
+| [MobileNetV2](../en/model_doc/mobilenet_v2) | ✅ | ❌ | ❌ |
+| [MobileViT](../en/model_doc/mobilevit) | ✅ | ✅ | ❌ |
+| [MobileViTV2](../en/model_doc/mobilevitv2) | ✅ | ❌ | ❌ |
+| [MPNet](../en/model_doc/mpnet) | ✅ | ✅ | ❌ |
+| [MPT](../en/model_doc/mpt) | ✅ | ❌ | ❌ |
+| [MRA](../en/model_doc/mra) | ✅ | ❌ | ❌ |
+| [MT5](../en/model_doc/mt5) | ✅ | ✅ | ✅ |
+| [MusicGen](../en/model_doc/musicgen) | ✅ | ❌ | ❌ |
+| [MVP](../en/model_doc/mvp) | ✅ | ❌ | ❌ |
+| [NAT](../en/model_doc/nat) | ✅ | ❌ | ❌ |
+| [Nezha](../en/model_doc/nezha) | ✅ | ❌ | ❌ |
+| [NLLB](../en/model_doc/nllb) | ✅ | ❌ | ❌ |
+| [NLLB-MOE](../en/model_doc/nllb-moe) | ✅ | ❌ | ❌ |
+| [Nougat](../en/model_doc/nougat) | ✅ | ✅ | ✅ |
+| [Nyströmformer](../en/model_doc/nystromformer) | ✅ | ❌ | ❌ |
+| [OneFormer](../en/model_doc/oneformer) | ✅ | ❌ | ❌ |
+| [OpenAI GPT](../en/model_doc/openai-gpt) | ✅ | ✅ | ❌ |
+| [OpenAI GPT-2](../en/model_doc/gpt2) | ✅ | ✅ | ✅ |
+| [OpenLlama](../en/model_doc/open-llama) | ✅ | ❌ | ❌ |
+| [OPT](../en/model_doc/opt) | ✅ | ✅ | ✅ |
+| [OWL-ViT](../en/model_doc/owlvit) | ✅ | ❌ | ❌ |
+| [OWLv2](../en/model_doc/owlv2) | ✅ | ❌ | ❌ |
+| [PatchTSMixer](../en/model_doc/patchtsmixer) | ✅ | ❌ | ❌ |
+| [PatchTST](../en/model_doc/patchtst) | ✅ | ❌ | ❌ |
+| [Pegasus](../en/model_doc/pegasus) | ✅ | ✅ | ✅ |
+| [PEGASUS-X](../en/model_doc/pegasus_x) | ✅ | ❌ | ❌ |
+| [Perceiver](../en/model_doc/perceiver) | ✅ | ❌ | ❌ |
+| [Persimmon](../en/model_doc/persimmon) | ✅ | ❌ | ❌ |
+| [Phi](../en/model_doc/phi) | ✅ | ❌ | ❌ |
+| [PhoBERT](../en/model_doc/phobert) | ✅ | ✅ | ✅ |
+| [Pix2Struct](../en/model_doc/pix2struct) | ✅ | ❌ | ❌ |
+| [PLBart](../en/model_doc/plbart) | ✅ | ❌ | ❌ |
+| [PoolFormer](../en/model_doc/poolformer) | ✅ | ❌ | ❌ |
+| [Pop2Piano](../en/model_doc/pop2piano) | ✅ | ❌ | ❌ |
+| [ProphetNet](../en/model_doc/prophetnet) | ✅ | ❌ | ❌ |
+| [PVT](../en/model_doc/pvt) | ✅ | ❌ | ❌ |
+| [QDQBert](../en/model_doc/qdqbert) | ✅ | ❌ | ❌ |
+| [Qwen2](../en/model_doc/qwen2) | ✅ | ❌ | ❌ |
+| [RAG](../en/model_doc/rag) | ✅ | ✅ | ❌ |
+| [REALM](../en/model_doc/realm) | ✅ | ❌ | ❌ |
+| [Reformer](../en/model_doc/reformer) | ✅ | ❌ | ❌ |
+| [RegNet](../en/model_doc/regnet) | ✅ | ✅ | ✅ |
+| [RemBERT](../en/model_doc/rembert) | ✅ | ✅ | ❌ |
+| [ResNet](../en/model_doc/resnet) | ✅ | ✅ | ✅ |
+| [RetriBERT](../en/model_doc/retribert) | ✅ | ❌ | ❌ |
+| [RoBERTa](../en/model_doc/roberta) | ✅ | ✅ | ✅ |
+| [RoBERTa-PreLayerNorm](../en/model_doc/roberta-prelayernorm) | ✅ | ✅ | ✅ |
+| [RoCBert](../en/model_doc/roc_bert) | ✅ | ❌ | ❌ |
+| [RoFormer](../en/model_doc/roformer) | ✅ | ✅ | ✅ |
+| [RWKV](../en/model_doc/rwkv) | ✅ | ❌ | ❌ |
+| [SAM](../en/model_doc/sam) | ✅ | ✅ | ❌ |
+| [SeamlessM4T](../en/model_doc/seamless_m4t) | ✅ | ❌ | ❌ |
+| [SeamlessM4Tv2](../en/model_doc/seamless_m4t_v2) | ✅ | ❌ | ❌ |
+| [SegFormer](../en/model_doc/segformer) | ✅ | ✅ | ❌ |
+| [SegGPT](../en/model_doc/seggpt) | ✅ | ❌ | ❌ |
+| [SEW](../en/model_doc/sew) | ✅ | ❌ | ❌ |
+| [SEW-D](../en/model_doc/sew-d) | ✅ | ❌ | ❌ |
+| [SigLIP](../en/model_doc/siglip) | ✅ | ❌ | ❌ |
+| [Speech Encoder decoder](../en/model_doc/speech-encoder-decoder) | ✅ | ❌ | ✅ |
+| [Speech2Text](../en/model_doc/speech_to_text) | ✅ | ✅ | ❌ |
+| [SpeechT5](../en/model_doc/speecht5) | ✅ | ❌ | ❌ |
+| [Splinter](../en/model_doc/splinter) | ✅ | ❌ | ❌ |
+| [SqueezeBERT](../en/model_doc/squeezebert) | ✅ | ❌ | ❌ |
+| [StableLm](../en/model_doc/stablelm) | ✅ | ❌ | ❌ |
+| [Starcoder2](../en/model_doc/starcoder2) | ✅ | ❌ | ❌ |
+| [SwiftFormer](../en/model_doc/swiftformer) | ✅ | ❌ | ❌ |
+| [Swin Transformer](../en/model_doc/swin) | ✅ | ✅ | ❌ |
+| [Swin Transformer V2](../en/model_doc/swinv2) | ✅ | ❌ | ❌ |
+| [Swin2SR](../en/model_doc/swin2sr) | ✅ | ❌ | ❌ |
+| [SwitchTransformers](../en/model_doc/switch_transformers) | ✅ | ❌ | ❌ |
+| [T5](../en/model_doc/t5) | ✅ | ✅ | ✅ |
+| [T5v1.1](../en/model_doc/t5v1.1) | ✅ | ✅ | ✅ |
+| [Table Transformer](../en/model_doc/table-transformer) | ✅ | ❌ | ❌ |
+| [TAPAS](../en/model_doc/tapas) | ✅ | ✅ | ❌ |
+| [TAPEX](../en/model_doc/tapex) | ✅ | ✅ | ✅ |
+| [Time Series Transformer](../en/model_doc/time_series_transformer) | ✅ | ❌ | ❌ |
+| [TimeSformer](../en/model_doc/timesformer) | ✅ | ❌ | ❌ |
+| [Trajectory Transformer](../en/model_doc/trajectory_transformer) | ✅ | ❌ | ❌ |
+| [Transformer-XL](../en/model_doc/transfo-xl) | ✅ | ✅ | ❌ |
+| [TrOCR](../en/model_doc/trocr) | ✅ | ❌ | ❌ |
+| [TVLT](../en/model_doc/tvlt) | ✅ | ❌ | ❌ |
+| [TVP](../en/model_doc/tvp) | ✅ | ❌ | ❌ |
+| [UL2](../en/model_doc/ul2) | ✅ | ✅ | ✅ |
+| [UMT5](../en/model_doc/umt5) | ✅ | ❌ | ❌ |
+| [UniSpeech](../en/model_doc/unispeech) | ✅ | ❌ | ❌ |
+| [UniSpeechSat](../en/model_doc/unispeech-sat) | ✅ | ❌ | ❌ |
+| [UnivNet](../en/model_doc/univnet) | ✅ | ❌ | ❌ |
+| [UPerNet](../en/model_doc/upernet) | ✅ | ❌ | ❌ |
+| [VAN](../en/model_doc/van) | ✅ | ❌ | ❌ |
+| [VideoMAE](../en/model_doc/videomae) | ✅ | ❌ | ❌ |
+| [ViLT](../en/model_doc/vilt) | ✅ | ❌ | ❌ |
+| [VipLlava](../en/model_doc/vipllava) | ✅ | ❌ | ❌ |
+| [Vision Encoder decoder](../en/model_doc/vision-encoder-decoder) | ✅ | ✅ | ✅ |
+| [VisionTextDualEncoder](../en/model_doc/vision-text-dual-encoder) | ✅ | ✅ | ✅ |
+| [VisualBERT](../en/model_doc/visual_bert) | ✅ | ❌ | ❌ |
+| [ViT](../en/model_doc/vit) | ✅ | ✅ | ✅ |
+| [ViT Hybrid](../en/model_doc/vit_hybrid) | ✅ | ❌ | ❌ |
+| [VitDet](../en/model_doc/vitdet) | ✅ | ❌ | ❌ |
+| [ViTMAE](../en/model_doc/vit_mae) | ✅ | ✅ | ❌ |
+| [ViTMatte](../en/model_doc/vitmatte) | ✅ | ❌ | ❌ |
+| [ViTMSN](../en/model_doc/vit_msn) | ✅ | ❌ | ❌ |
+| [VITS](../en/model_doc/vits) | ✅ | ❌ | ❌ |
+| [ViViT](../en/model_doc/vivit) | ✅ | ❌ | ❌ |
+| [Wav2Vec2](../en/model_doc/wav2vec2) | ✅ | ✅ | ✅ |
+| [Wav2Vec2-BERT](../en/model_doc/wav2vec2-bert) | ✅ | ❌ | ❌ |
+| [Wav2Vec2-Conformer](../en/model_doc/wav2vec2-conformer) | ✅ | ❌ | ❌ |
+| [Wav2Vec2Phoneme](../en/model_doc/wav2vec2_phoneme) | ✅ | ✅ | ✅ |
+| [WavLM](../en/model_doc/wavlm) | ✅ | ❌ | ❌ |
+| [Whisper](../en/model_doc/whisper) | ✅ | ✅ | ✅ |
+| [X-CLIP](../en/model_doc/xclip) | ✅ | ❌ | ❌ |
+| [X-MOD](../en/model_doc/xmod) | ✅ | ❌ | ❌ |
+| [XGLM](../en/model_doc/xglm) | ✅ | ✅ | ✅ |
+| [XLM](../en/model_doc/xlm) | ✅ | ✅ | ❌ |
+| [XLM-ProphetNet](../en/model_doc/xlm-prophetnet) | ✅ | ❌ | ❌ |
+| [XLM-RoBERTa](../en/model_doc/xlm-roberta) | ✅ | ✅ | ✅ |
+| [XLM-RoBERTa-XL](../en/model_doc/xlm-roberta-xl) | ✅ | ❌ | ❌ |
+| [XLM-V](../en/model_doc/xlm-v) | ✅ | ✅ | ✅ |
+| [XLNet](../en/model_doc/xlnet) | ✅ | ✅ | ❌ |
+| [XLS-R](../en/model_doc/xls_r) | ✅ | ✅ | ✅ |
+| [XLSR-Wav2Vec2](../en/model_doc/xlsr_wav2vec2) | ✅ | ✅ | ✅ |
+| [YOLOS](../en/model_doc/yolos) | ✅ | ❌ | ❌ |
+| [YOSO](../en/model_doc/yoso) | ✅ | ❌ | ❌ |
+
+
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/installation.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/installation.md
new file mode 100644
index 0000000000000000000000000000000000000000..a9102f3393a740a988d88be496b6835e03575d57
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/installation.md
@@ -0,0 +1,256 @@
+
+
+# 安装
+
+为你正在使用的深度学习框架安装 🤗 Transformers、设置缓存，并选择性配置 🤗 Transformers 以离线运行。
+
+🤗 Transformers 已在 Python 3.6+、PyTorch 1.1.0+、TensorFlow 2.0+ 以及 Flax 上进行测试。针对你使用的深度学习框架，请参照以下安装说明进行安装：
+
+* [PyTorch](https://pytorch.org/get-started/locally/) 安装说明。
+* [TensorFlow 2.0](https://www.tensorflow.org/install/pip) 安装说明。
+* [Flax](https://flax.readthedocs.io/en/latest/) 安装说明。
+
+## 使用 pip 安装
+
+你应该使用 [虚拟环境](https://docs.python.org/3/library/venv.html) 安装 🤗 Transformers。如果你不熟悉 Python 虚拟环境，请查看此 [教程](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)。使用虚拟环境，你可以轻松管理不同项目，避免不同依赖项之间的兼容性问题。
+
+首先，在项目目录中创建虚拟环境：
+
+```bash
+python -m venv .env
+```
+
+在 Linux 和 MacOs 系统中激活虚拟环境：
+
+```bash
+source .env/bin/activate
+```
+在 Windows 系统中激活虚拟环境：
+
+```bash
+.env/Scripts/activate
+```
+
+现在你可以使用以下命令安装 🤗 Transformers：
+
+```bash
+pip install transformers
+```
+
+若仅需 CPU 支持，可以使用单行命令方便地安装 🤗 Transformers 和深度学习库。例如，使用以下命令安装 🤗 Transformers 和 PyTorch：
+
+```bash
+pip install 'transformers[torch]'
+```
+
+🤗 Transformers 和 TensorFlow 2.0：
+
+```bash
+pip install 'transformers[tf-cpu]'
+```
+
+
+
+[[autodoc]] get_cosine_schedule_with_warmup
+
+
+
+[[autodoc]] get_cosine_with_hard_restarts_schedule_with_warmup
+
+
+
+[[autodoc]] get_linear_schedule_with_warmup
+
+
+
+[[autodoc]] get_polynomial_decay_schedule_with_warmup
+
+[[autodoc]] get_inverse_sqrt_schedule
+
+### Warmup (TensorFlow)
+
+[[autodoc]] WarmUp
+
+## Gradient Strategies
+
+### GradientAccumulator (TensorFlow)
+
+[[autodoc]] GradientAccumulator
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/main_classes/output.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/main_classes/output.md
new file mode 100644
index 0000000000000000000000000000000000000000..f4d5c3c6941d515e87e8628f499dbd23aea701d9
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/main_classes/output.md
@@ -0,0 +1,309 @@
+
+
+# 模型输出
+
+所有模型的输出都是 [`~utils.ModelOutput`] 的子类的实例。这些是包含模型返回的所有信息的数据结构，但也可以用作元组或字典。
+
+让我们看一个例子：
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+tokenizer = BertTokenizer.from_pretrained("google-bert/bert-base-uncased")
+model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased")
+
+inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
+outputs = model(**inputs, labels=labels)
+```
+
+`outputs` 对象是 [`~modeling_outputs.SequenceClassifierOutput`]，如下面该类的文档中所示，它表示它有一个可选的 `loss`，一个 `logits`，一个可选的 `hidden_states` 和一个可选的 `attentions` 属性。在这里，我们有 `loss`，因为我们传递了 `labels`，但我们没有 `hidden_states` 和 `attentions`，因为我们没有传递 `output_hidden_states=True` 或 `output_attentions=True`。
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+你可以在[此链接](https://github.com/huggingface/optimum-benchmark/tree/main/examples/running-mistrals)中找到完整的结果以及包版本。
+
+从结果来看，AWQ量化方法是推理、文本生成中最快的量化方法，并且在文本生成的峰值内存方面属于最低。然而，对于每批数据，AWQ似乎有最大的前向延迟。
+
+
+### Google colab 演示
+
+查看如何在[Google Colab演示](https://colab.research.google.com/drive/1HzZH89yAXJaZgwJDhQj9LqSBux932BvY)中使用此集成！
+
+
+### AwqConfig
+
+[[autodoc]] AwqConfig
+
+## `AutoGPTQ` 集成
+
+🤗 Transformers已经整合了`optimum` API，用于对语言模型执行GPTQ量化。您可以以8、4、3甚至2位加载和量化您的模型，而性能无明显下降，并且推理速度更快！这受到大多数GPU硬件的支持。
+
+要了解更多关于量化模型的信息，请查看：
+- [GPTQ](https://huggingface.co/papers/2210.17323)论文
+- `optimum`关于GPTQ量化的[指南](https://huggingface.co/docs/optimum/llm_quantization/usage_guides/quantization)
+- 用作后端的[`AutoGPTQ`](https://github.com/PanQiWei/AutoGPTQ)库
+
+
+### 要求
+
+为了运行下面的代码，您需要安装：
+
+- 安装最新版本的 `AutoGPTQ` 库
+`pip install auto-gptq`
+
+- 从源代码安装最新版本的`optimum`
+`pip install git+https://github.com/huggingface/optimum.git`
+
+- 从源代码安装最新版本的`transformers`
+`pip install git+https://github.com/huggingface/transformers.git`
+
+- 安装最新版本的`accelerate`库：
+`pip install --upgrade accelerate`
+
+请注意，目前GPTQ集成仅支持文本模型，对于视觉、语音或多模态模型可能会遇到预期以外结果。
+
+### 加载和量化模型
+
+GPTQ是一种在使用量化模型之前需要进行权重校准的量化方法。如果您想从头开始对transformers模型进行量化，生成量化模型可能需要一些时间（在Google Colab上对`facebook/opt-350m`模型量化约为5分钟）。
+
+因此，有两种不同的情况下您可能想使用GPTQ量化模型。第一种情况是加载已经由其他用户在Hub上量化的模型，第二种情况是从头开始对您的模型进行量化并保存或推送到Hub，以便其他用户也可以使用它。
+
+
+#### GPTQ 配置
+
+为了加载和量化一个模型，您需要创建一个[`GPTQConfig`]。您需要传递`bits`的数量，一个用于校准量化的`dataset`，以及模型的`tokenizer`以准备数据集。
+
+```python
+model_id = "facebook/opt-125m"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+gptq_config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer)
+```
+
+请注意，您可以将自己的数据集以字符串列表形式传递到模型。然而，强烈建议您使用GPTQ论文中提供的数据集。
+
+
+```python
+dataset = ["auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."]
+quantization = GPTQConfig(bits=4, dataset = dataset, tokenizer=tokenizer)
+```
+
+#### 量化
+
+您可以通过使用`from_pretrained`并设置`quantization_config`来对模型进行量化。
+
+```python
+from transformers import AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=gptq_config)
+
+```
+
+请注意，您需要一个GPU来量化模型。我们将模型放在cpu中，并将模块来回移动到gpu中，以便对其进行量化。
+
+如果您想在使用 CPU 卸载的同时最大化 GPU 使用率，您可以设置 `device_map = "auto"`。
+
+
+```python
+from transformers import AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=gptq_config)
+```
+
+请注意，不支持磁盘卸载。此外，如果由于数据集而内存不足，您可能需要在`from_pretrained`中设置`max_memory`。查看这个[指南](https://huggingface.co/docs/accelerate/usage_guides/big_modeling#designing-a-device-map)以了解有关`device_map`和`max_memory`的更多信息。
+
+
+
+
+
+
+
+要在 `Trainer` 中使用它，只需在创建 `TrainingArguments` 实例时传递 `neftune_noise_alpha`。请注意，为了避免任何意外行为，NEFTune在训练后被禁止，以此恢复原始的embedding层。
+
+```python
+from transformers import Trainer, TrainingArguments
+
+args = TrainingArguments(..., neftune_noise_alpha=0.1)
+trainer = Trainer(..., args=args)
+
+...
+
+trainer.train()
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/model_doc/bert.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/model_doc/bert.md
new file mode 100644
index 0000000000000000000000000000000000000000..db61b675e49f1b0ee06bc6f4728e05fd1ffb1184
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/model_doc/bert.md
@@ -0,0 +1,258 @@
+
+
+
+
+
+
+# BERT
+
+[BERT](https://huggingface.co/papers/1810.04805) 是一个在无标签的文本数据上预训练的双向 transformer，用于预测句子中被掩码的（masked） token，以及预测一个句子是否跟随在另一个句子之后。其主要思想是，在预训练过程中，通过随机掩码一些 token，让模型利用左右上下文的信息预测它们，从而获得更全面深入的理解。此外，BERT 具有很强的通用性，其学习到的语言表示可以通过额外的层或头进行微调，从而适配其他下游 NLP 任务。
+
+你可以在 [BERT](https://huggingface.co/collections/google/bert-release-64ff5e7a4be99045d1896dbc) 集合下找到 BERT 的所有原始 checkpoint。
+
+> [!TIP]
+> 点击右侧边栏中的 BERT 模型，以查看将 BERT 应用于不同语言任务的更多示例。
+
+下面的示例演示了如何使用 [`Pipeline`], [`AutoModel`] 和命令行预测 `[MASK]` token。
+
+
+ 
+ 
+ 
+ 
+
+
+
+
+
+
+ 
+ 与完整尺寸的模型权重（约为700MB）相比，存储在Hub上的OPTForCausalLM模型的adapter权重仅为~6MB。
+
+
+如果您对学习更多关于🤗 PEFT库感兴趣，请查看[文档](https://huggingface.co/docs/peft/index)。
+
+
+## 设置
+
+首先安装 🤗 PEFT：
+
+```bash
+pip install peft
+```
+
+如果你想尝试全新的特性，你可能会有兴趣从源代码安装这个库：
+
+```bash
+pip install git+https://github.com/huggingface/peft.git
+```
+## 支持的 PEFT 模型
+
+Transformers原生支持一些PEFT方法，这意味着你可以加载本地存储或在Hub上的adapter权重，并使用几行代码轻松运行或训练它们。以下是受支持的方法：
+
+- [Low Rank Adapters](https://huggingface.co/docs/peft/conceptual_guides/lora)
+- [IA3](https://huggingface.co/docs/peft/conceptual_guides/ia3)
+- [AdaLoRA](https://huggingface.co/papers/2303.10512)
+
+如果你想使用其他PEFT方法，例如提示学习或提示微调，或者关于通用的 🤗 PEFT库，请参阅[文档](https://huggingface.co/docs/peft/index)。
+
+## 加载 PEFT adapter
+
+要从huggingface的Transformers库中加载并使用PEFTadapter模型，请确保Hub仓库或本地目录包含一个`adapter_config.json`文件和adapter权重，如上例所示。然后，您可以使用`AutoModelFor`类加载PEFT adapter模型。例如，要为因果语言建模加载一个PEFT adapter模型：
+
+1. 指定PEFT模型id
+2. 将其传递给[`AutoModelForCausalLM`]类
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+peft_model_id = "ybelkada/opt-350m-lora"
+model = AutoModelForCausalLM.from_pretrained(peft_model_id)
+```
+
+
+
+
+
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/perf_torch_compile.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/perf_torch_compile.md
new file mode 100644
index 0000000000000000000000000000000000000000..80c32adab213d50ce7055fc0d469fff0f9c05cd6
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/perf_torch_compile.md
@@ -0,0 +1,362 @@
+
+
+# 使用 torch.compile() 优化推理
+
+本指南旨在为使用[`torch.compile()`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html)在[🤗 Transformers中的计算机视觉模型](https://huggingface.co/models?pipeline_tag=image-classification&library=transformers&sort=trending)中引入的推理速度提升提供一个基准。
+
+
+## torch.compile 的优势
+
+根据模型和GPU的不同，`torch.compile()`在推理过程中可以提高多达30%的速度。要使用`torch.compile()`，只需安装2.0及以上版本的`torch`即可。
+
+编译模型需要时间，因此如果您只需要编译一次模型而不是每次推理都编译，那么它非常有用。
+要编译您选择的任何计算机视觉模型，请按照以下方式调用`torch.compile()`：
+
+
+```diff
+from transformers import AutoModelForImageClassification
+
+model = AutoModelForImageClassification.from_pretrained(MODEL_ID).to("cuda")
++ model = torch.compile(model)
+```
+
+`compile()` 提供了多种编译模式，它们在编译时间和推理开销上有所不同。`max-autotune` 比 `reduce-overhead` 需要更长的时间，但会得到更快的推理速度。默认模式在编译时最快，但在推理时间上与 `reduce-overhead` 相比效率较低。在本指南中，我们使用了默认模式。您可以在[这里](https://pytorch.org/get-started/pytorch-2.0/#user-experience)了解更多信息。
+
+我们在 PyTorch 2.0.1 版本上使用不同的计算机视觉模型、任务、硬件类型和数据批量大小对 `torch.compile` 进行了基准测试。
+
+## 基准测试代码
+
+以下是每个任务的基准测试代码。我们在推理之前”预热“GPU，并取300次推理的平均值，每次使用相同的图像。
+
+### 使用 ViT 进行图像分类
+
+```python
+import torch
+from PIL import Image
+import requests
+import numpy as np
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
+image = Image.open(requests.get(url, stream=True).raw)
+
+processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
+model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224").to("cuda")
+model = torch.compile(model)
+
+processed_input = processor(image, return_tensors='pt').to(device="cuda")
+
+with torch.no_grad():
+ _ = model(**processed_input)
+
+```
+
+#### 使用 DETR 进行目标检测
+
+```python
+from transformers import AutoImageProcessor, AutoModelForObjectDetection
+
+processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
+model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50").to("cuda")
+model = torch.compile(model)
+
+texts = ["a photo of a cat", "a photo of a dog"]
+inputs = processor(text=texts, images=image, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+ _ = model(**inputs)
+```
+
+#### 使用 Segformer 进行图像分割
+
+```python
+from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation
+
+processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
+model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to("cuda")
+model = torch.compile(model)
+seg_inputs = processor(images=image, return_tensors="pt").to("cuda")
+
+with torch.no_grad():
+ _ = model(**seg_inputs)
+```
+
+以下是我们进行基准测试的模型列表。
+
+**图像分类**
+- [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)
+- [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k)
+- [facebook/convnext-large-224](https://huggingface.co/facebook/convnext-large-224)
+- [microsoft/resnet-50](https://huggingface.co/)
+
+**图像分割**
+- [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [facebook/mask2former-swin-tiny-coco-panoptic](https://huggingface.co/facebook/mask2former-swin-tiny-coco-panoptic)
+- [facebook/maskformer-swin-base-ade](https://huggingface.co/facebook/maskformer-swin-base-ade)
+- [google/deeplabv3_mobilenet_v2_1.0_513](https://huggingface.co/google/deeplabv3_mobilenet_v2_1.0_513)
+
+**目标检测**
+- [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32)
+- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101)
+- [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50)
+
+ 下面是使用和不使用`torch.compile()`的推理持续时间可视化，以及每个模型在不同硬件和数据批量大小下的改进百分比。
+
+
+
+
+
+
+
+ 
+
+
+
+ 
+
+
+
+ 
+
+
+
+
+
+
+
+
+
+
+下面可以找到每个模型使用和不使用`compile()`的推理时间（毫秒）。请注意，OwlViT在大批量大小下会导致内存溢出。
+
+### A100 (batch size: 1)
+
+| **Task/Model** | **torch 2.0 -
+ 
+
+
+
+ 
+
+
+ no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:| +| Image Classification/ViT | 9.325 | 7.584 | +| Image Segmentation/Segformer | 11.759 | 10.500 | +| Object Detection/OwlViT | 24.978 | 18.420 | +| Image Classification/BeiT | 11.282 | 8.448 | +| Object Detection/DETR | 34.619 | 19.040 | +| Image Classification/ConvNeXT | 10.410 | 10.208 | +| Image Classification/ResNet | 6.531 | 4.124 | +| Image Segmentation/Mask2former | 60.188 | 49.117 | +| Image Segmentation/Maskformer | 75.764 | 59.487 | +| Image Segmentation/MobileNet | 8.583 | 3.974 | +| Object Detection/Resnet-101 | 36.276 | 18.197 | +| Object Detection/Conditional-DETR | 31.219 | 17.993 | + + +### A100 (batch size: 4) + +| **Task/Model** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:| +| Image Classification/ViT | 14.832 | 14.499 | +| Image Segmentation/Segformer | 18.838 | 16.476 | +| Image Classification/BeiT | 13.205 | 13.048 | +| Object Detection/DETR | 48.657 | 32.418| +| Image Classification/ConvNeXT | 22.940 | 21.631 | +| Image Classification/ResNet | 6.657 | 4.268 | +| Image Segmentation/Mask2former | 74.277 | 61.781 | +| Image Segmentation/Maskformer | 180.700 | 159.116 | +| Image Segmentation/MobileNet | 14.174 | 8.515 | +| Object Detection/Resnet-101 | 68.101 | 44.998 | +| Object Detection/Conditional-DETR | 56.470 | 35.552 | + +### A100 (batch size: 16) + +| **Task/Model** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:| +| Image Classification/ViT | 40.944 | 40.010 | +| Image Segmentation/Segformer | 37.005 | 31.144 | +| Image Classification/BeiT | 41.854 | 41.048 | +| Object Detection/DETR | 164.382 | 161.902 | +| Image Classification/ConvNeXT | 82.258 | 75.561 | +| Image Classification/ResNet | 7.018 | 5.024 | +| Image Segmentation/Mask2former | 178.945 | 154.814 | +| Image Segmentation/Maskformer | 638.570 | 579.826 | +| Image Segmentation/MobileNet | 51.693 | 30.310 | +| Object Detection/Resnet-101 | 232.887 | 155.021 | +| Object Detection/Conditional-DETR | 180.491 | 124.032 | + +### V100 (batch size: 1) + +| **Task/Model** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:| +| Image Classification/ViT | 10.495 | 6.00 | +| Image Segmentation/Segformer | 13.321 | 5.862 | +| Object Detection/OwlViT | 25.769 | 22.395 | +| Image Classification/BeiT | 11.347 | 7.234 | +| Object Detection/DETR | 33.951 | 19.388 | +| Image Classification/ConvNeXT | 11.623 | 10.412 | +| Image Classification/ResNet | 6.484 | 3.820 | +| Image Segmentation/Mask2former | 64.640 | 49.873 | +| Image Segmentation/Maskformer | 95.532 | 72.207 | +| Image Segmentation/MobileNet | 9.217 | 4.753 | +| Object Detection/Resnet-101 | 52.818 | 28.367 | +| Object Detection/Conditional-DETR | 39.512 | 20.816 | + +### V100 (batch size: 4) + +| **Task/Model** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:| +| Image Classification/ViT | 15.181 | 14.501 | +| Image Segmentation/Segformer | 16.787 | 16.188 | +| Image Classification/BeiT | 15.171 | 14.753 | +| Object Detection/DETR | 88.529 | 64.195 | +| Image Classification/ConvNeXT | 29.574 | 27.085 | +| Image Classification/ResNet | 6.109 | 4.731 | +| Image Segmentation/Mask2former | 90.402 | 76.926 | +| Image Segmentation/Maskformer | 234.261 | 205.456 | +| Image Segmentation/MobileNet | 24.623 | 14.816 | +| Object Detection/Resnet-101 | 134.672 | 101.304 | +| Object Detection/Conditional-DETR | 97.464 | 69.739 | + +### V100 (batch size: 16) + +| **Task/Model** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:| +| Image Classification/ViT | 52.209 | 51.633 | +| Image Segmentation/Segformer | 61.013 | 55.499 | +| Image Classification/BeiT | 53.938 | 53.581 | +| Object Detection/DETR | OOM | OOM | +| Image Classification/ConvNeXT | 109.682 | 100.771 | +| Image Classification/ResNet | 14.857 | 12.089 | +| Image Segmentation/Mask2former | 249.605 | 222.801 | +| Image Segmentation/Maskformer | 831.142 | 743.645 | +| Image Segmentation/MobileNet | 93.129 | 55.365 | +| Object Detection/Resnet-101 | 482.425 | 361.843 | +| Object Detection/Conditional-DETR | 344.661 | 255.298 | + +### T4 (batch size: 1) + +| **Task/Model** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:| +| Image Classification/ViT | 16.520 | 15.786 | +| Image Segmentation/Segformer | 16.116 | 14.205 | +| Object Detection/OwlViT | 53.634 | 51.105 | +| Image Classification/BeiT | 16.464 | 15.710 | +| Object Detection/DETR | 73.100 | 53.99 | +| Image Classification/ConvNeXT | 32.932 | 30.845 | +| Image Classification/ResNet | 6.031 | 4.321 | +| Image Segmentation/Mask2former | 79.192 | 66.815 | +| Image Segmentation/Maskformer | 200.026 | 188.268 | +| Image Segmentation/MobileNet | 18.908 | 11.997 | +| Object Detection/Resnet-101 | 106.622 | 82.566 | +| Object Detection/Conditional-DETR | 77.594 | 56.984 | + +### T4 (batch size: 4) + +| **Task/Model** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:| +| Image Classification/ViT | 43.653 | 43.626 | +| Image Segmentation/Segformer | 45.327 | 42.445 | +| Image Classification/BeiT | 52.007 | 51.354 | +| Object Detection/DETR | 277.850 | 268.003 | +| Image Classification/ConvNeXT | 119.259 | 105.580 | +| Image Classification/ResNet | 13.039 | 11.388 | +| Image Segmentation/Mask2former | 201.540 | 184.670 | +| Image Segmentation/Maskformer | 764.052 | 711.280 | +| Image Segmentation/MobileNet | 74.289 | 48.677 | +| Object Detection/Resnet-101 | 421.859 | 357.614 | +| Object Detection/Conditional-DETR | 289.002 | 226.945 | + +### T4 (batch size: 16) + +| **Task/Model** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:| +| Image Classification/ViT | 163.914 | 160.907 | +| Image Segmentation/Segformer | 192.412 | 163.620 | +| Image Classification/BeiT | 188.978 | 187.976 | +| Object Detection/DETR | OOM | OOM | +| Image Classification/ConvNeXT | 422.886 | 388.078 | +| Image Classification/ResNet | 44.114 | 37.604 | +| Image Segmentation/Mask2former | 756.337 | 695.291 | +| Image Segmentation/Maskformer | 2842.940 | 2656.88 | +| Image Segmentation/MobileNet | 299.003 | 201.942 | +| Object Detection/Resnet-101 | 1619.505 | 1262.758 | +| Object Detection/Conditional-DETR | 1137.513 | 897.390| + +## PyTorch Nightly +我们还在 PyTorch Nightly 版本（2.1.0dev）上进行了基准测试，可以在[这里](https://download.pytorch.org/whl/nightly/cu118)找到 Nightly 版本的安装包，并观察到了未编译和编译模型的延迟性能改善。 + +### A100 + +| **Task/Model** | **Batch Size** | **torch 2.0 - no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:|:---:| +| Image Classification/BeiT | Unbatched | 12.462 | 6.954 | +| Image Classification/BeiT | 4 | 14.109 | 12.851 | +| Image Classification/BeiT | 16 | 42.179 | 42.147 | +| Object Detection/DETR | Unbatched | 30.484 | 15.221 | +| Object Detection/DETR | 4 | 46.816 | 30.942 | +| Object Detection/DETR | 16 | 163.749 | 163.706 | + +### T4 + +| **Task/Model** | **Batch Size** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:|:---:| +| Image Classification/BeiT | Unbatched | 14.408 | 14.052 | +| Image Classification/BeiT | 4 | 47.381 | 46.604 | +| Image Classification/BeiT | 16 | 42.179 | 42.147 | +| Object Detection/DETR | Unbatched | 68.382 | 53.481 | +| Object Detection/DETR | 4 | 269.615 | 204.785 | +| Object Detection/DETR | 16 | OOM | OOM | + +### V100 + +| **Task/Model** | **Batch Size** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:|:---:| +| Image Classification/BeiT | Unbatched | 13.477 | 7.926 | +| Image Classification/BeiT | 4 | 15.103 | 14.378 | +| Image Classification/BeiT | 16 | 52.517 | 51.691 | +| Object Detection/DETR | Unbatched | 28.706 | 19.077 | +| Object Detection/DETR | 4 | 88.402 | 62.949| +| Object Detection/DETR | 16 | OOM | OOM | + + +## 降低开销 +我们在 PyTorch Nightly 版本中为 A100 和 T4 进行了 `reduce-overhead` 编译模式的性能基准测试。 + +### A100 + +| **Task/Model** | **Batch Size** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:|:---:| +| Image Classification/ConvNeXT | Unbatched | 11.758 | 7.335 | +| Image Classification/ConvNeXT | 4 | 23.171 | 21.490 | +| Image Classification/ResNet | Unbatched | 7.435 | 3.801 | +| Image Classification/ResNet | 4 | 7.261 | 2.187 | +| Object Detection/Conditional-DETR | Unbatched | 32.823 | 11.627 | +| Object Detection/Conditional-DETR | 4 | 50.622 | 33.831 | +| Image Segmentation/MobileNet | Unbatched | 9.869 | 4.244 | +| Image Segmentation/MobileNet | 4 | 14.385 | 7.946 | + + +### T4 + +| **Task/Model** | **Batch Size** | **torch 2.0 -
no compile** | **torch 2.0 -
compile** | +|:---:|:---:|:---:|:---:| +| Image Classification/ConvNeXT | Unbatched | 32.137 | 31.84 | +| Image Classification/ConvNeXT | 4 | 120.944 | 110.209 | +| Image Classification/ResNet | Unbatched | 9.761 | 7.698 | +| Image Classification/ResNet | 4 | 15.215 | 13.871 | +| Object Detection/Conditional-DETR | Unbatched | 72.150 | 57.660 | +| Object Detection/Conditional-DETR | 4 | 301.494 | 247.543 | +| Image Segmentation/MobileNet | Unbatched | 22.266 | 19.339 | +| Image Segmentation/MobileNet | 4 | 78.311 | 50.983 | + + diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/perf_train_cpu.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/perf_train_cpu.md new file mode 100644 index 0000000000000000000000000000000000000000..f576c3fa855f21df3ac9b7200a4fee8d4aca6b8c --- /dev/null +++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/perf_train_cpu.md @@ -0,0 +1,85 @@ + + +# 在CPU上进行高效训练 + +本指南将重点介绍如何在CPU上高效训练大型模型。 + +## 使用IPEX进行混合精度训练 +混合精度训练在模型中可以同时使用单精度（fp32）和半精度（bf16/fp16）的数据类型来加速训练或推理过程，并且仍然能保留大部分单精度的准确性。现代的CPU，例如第三代、第四代和第五代Intel® Xeon® Scalable处理器，原生支持bf16，而第六代Intel® Xeon® Scalable处理器原生支持bf16和fp16。您在训练时启用bf16或fp16的混合精度训练可以直接提高处理性能。 + +为了进一步最大化训练性能，您可以使用Intel® PyTorch扩展（IPEX）。IPEX是一个基于PyTorch构建的库，增加了额外的CPU指令集架构（ISA）级别的支持，比如Intel®高级向量扩展512（Intel® AVX512-VNNI）和Intel®高级矩阵扩展（Intel® AMX）。这为Intel CPU提供额外的性能提升。然而，仅支持AVX2的CPU（例如AMD或较旧的Intel CPU）在使用IPEX时并不保证能提高性能。 + +从PyTorch 1.10版本起，CPU后端已经启用了自动混合精度（AMP）。IPEX还支持bf16/fp16的AMP和bf16/fp16算子优化，并且部分功能已经上游到PyTorch主分支。通过IPEX AMP，您可以获得更好的性能和用户体验。 + +点击[这里](https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/features/amp.html)查看**自动混合精度**的更多详细信息。 + + +### IPEX 安装: + +IPEX 的发布与 PyTorch 一致，您可以通过 pip 安装： + +| PyTorch Version | IPEX version | +| :---------------: | :----------: | +| 2.5.0 | 2.5.0+cpu | +| 2.4.0 | 2.4.0+cpu | +| 2.3.0 | 2.3.0+cpu | +| 2.2.0 | 2.2.0+cpu | + +请运行 `pip list | grep torch` 以获取您的 `pytorch_version`，然后根据该版本安装相应的 `IPEX version_name`。 +```bash +pip install intel_extension_for_pytorch==
python examples/pytorch/question-answering/run_qa.py \ +--model_name_or_path google-bert/bert-base-uncased \ +--dataset_name squad \ +--do_train \ +--do_eval \ +--per_device_train_batch_size 12 \ +--learning_rate 3e-5 \ +--num_train_epochs 2 \ +--max_seq_length 384 \ +--doc_stride 128 \ +--output_dir /tmp/debug_squad/ \ +--use_ipex \ +--bf16 \ +--use_cpu+ +如果您想在脚本中启用 `use_ipex` 和 `bf16`，请像下面这样将这些参数添加到 `TrainingArguments` 中： +```diff +training_args = TrainingArguments( + output_dir=args.output_path, ++ bf16=True, ++ use_ipex=True, ++ use_cpu=True, + **kwargs +) +``` + +### 实践示例 + +博客: [使用 Intel Sapphire Rapids 加速 PyTorch Transformers](https://huggingface.co/blog/intel-sapphire-rapids) diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/perf_train_special.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/perf_train_special.md new file mode 100644 index 0000000000000000000000000000000000000000..ee8553475679ee19d3c04fecb71f60924e11e731 --- /dev/null +++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/docs/source/zh/perf_train_special.md @@ -0,0 +1,58 @@ + + +# 在 Apple Silicon 芯片上进行 PyTorch 训练 + +之前，在 Mac 上训练模型仅限于使用 CPU 训练。不过随着PyTorch v1.12的发布，您可以通过在 Apple Silicon 芯片的 GPU 上训练模型来显著提高性能和训练速度。这是通过将 Apple 的 Metal 性能着色器 (Metal Performance Shaders, MPS) 作为后端集成到PyTorch中实现的。[MPS后端](https://pytorch.org/docs/stable/notes/mps.html) 将 PyTorch 操作视为自定义的 Metal 着色器来实现，并将对应模块部署到`mps`设备上。 + +
+ +如果您遇到任何其他错误，请在[PyTorch库](https://github.com/pytorch/pytorch/issues)中创建一个 issue，因为[`Trainer`]类中只集成了 MPS 后端. + +
+ 
+
+
+使用 [`AutoImageProcessor.from_pretrained`] 加载`image processor`：
+
+```py
+>>> from transformers import AutoImageProcessor
+
+>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
+```
+
+首先，让我们进行图像增强。您可以使用任何您喜欢的库，但在本教程中，我们将使用torchvision的[`transforms`](https://pytorch.org/vision/stable/transforms.html)模块。如果您有兴趣使用其他数据增强库，请参阅[Albumentations](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb)或[Kornia notebooks](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb)中的示例。
+
+1. 在这里，我们使用[`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html)将[`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html)和 [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html)变换连接在一起。请注意，对于调整大小，我们可以从`image_processor`中获取图像尺寸要求。对于一些模型，精确的高度和宽度需要被定义，对于其他模型只需定义`shortest_edge`。
+
+
+```py
+>>> from torchvision.transforms import RandomResizedCrop, ColorJitter, Compose
+
+>>> size = (
+... image_processor.size["shortest_edge"]
+... if "shortest_edge" in image_processor.size
+... else (image_processor.size["height"], image_processor.size["width"])
+... )
+
+>>> _transforms = Compose([RandomResizedCrop(size), ColorJitter(brightness=0.5, hue=0.5)])
+```
+
+2. 模型接受 [`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values) 作为输入。`ImageProcessor` 可以进行图像的标准化，并生成适当的张量。创建一个函数，将图像增强和图像预处理步骤组合起来处理批量图像，并生成 `pixel_values`：
+
+
+```py
+>>> def transforms(examples):
+... images = [_transforms(img.convert("RGB")) for img in examples["image"]]
+... examples["pixel_values"] = image_processor(images, do_resize=False, return_tensors="pt")["pixel_values"]
+... return examples
+```
+
+
+
+ 
+
+
+
+
+
+
+然后切换您clone的 🤗 Transformers 仓到特定的版本，例如v3.5.1：
+
+```bash
+git checkout tags/v3.5.1
+```
+
+在安装了正确的库版本后，进入您选择的版本的`example`文件夹并安装例子要求的环境：
+
+```bash
+pip install -r requirements.txt
+```
+
+## 运行脚本
+
+老版本🤗 Transformers示例
+ +
+
+
+Alternatively, you can switch your cloned 🤗 Transformers to a specific version (for instance with v3.5.1) with
+```bash
+git checkout tags/v3.5.1
+```
+and run the example command as usual afterward.
+
+## Running the Examples on Remote Hardware with Auto-Setup
+
+[run_on_remote.py](./run_on_remote.py) is a script that launches any example on remote self-hosted hardware,
+with automatic hardware and environment setup. It uses [Runhouse](https://github.com/run-house/runhouse) to launch
+on self-hosted hardware (e.g. in your own cloud account or on-premise cluster) but there are other options
+for running remotely as well. You can easily customize the example used, command line arguments, dependencies,
+and type of compute hardware, and then run the script to automatically launch the example.
+
+You can refer to
+[hardware setup](https://www.run.house/docs/tutorials/quick-start-cloud)
+for more information about hardware and dependency setup with Runhouse, or this
+[Colab tutorial](https://colab.research.google.com/drive/1sh_aNQzJX5BKAdNeXthTNGxKz7sM9VPc) for a more in-depth
+walkthrough.
+
+You can run the script with the following commands:
+
+```bash
+# First install runhouse:
+pip install runhouse
+
+# For an on-demand V100 with whichever cloud provider you have configured:
+python run_on_remote.py \
+ --example pytorch/text-generation/run_generation.py \
+ --model_type=gpt2 \
+ --model_name_or_path=openai-community/gpt2 \
+ --prompt "I am a language model and"
+
+# For byo (bring your own) cluster:
+python run_on_remote.py --host Examples for older versions of 🤗 Transformers
+-
+
- v4.21.0 +
- v4.20.1 +
- v4.19.4 +
- v4.18.0 +
- v4.17.0 +
- v4.16.2 +
- v4.15.0 +
- v4.14.1 +
- v4.13.0 +
- v4.12.5 +
- v4.11.3 +
- v4.10.3 +
- v4.9.2 +
- v4.8.2 +
- v4.7.0 +
- v4.6.1 +
- v4.5.1 +
- v4.4.2 +
- v4.3.3 +
- v4.2.2 +
- v4.1.1 +
- v4.0.1 +
- v3.5.1 +
- v3.4.0 +
- v3.3.1 +
- v3.2.0 +
- v3.1.0 +
- v3.0.2 +
- v2.11.0 +
- v2.10.0 +
- v2.9.1 +
- v2.8.0 +
- v2.7.0 +
- v2.6.0 +
- v2.5.1 +
- v2.4.0 +
- v2.3.0 +
- v2.2.0 +
- v2.1.1 +
- v2.0.0 +
- v1.2.0 +
- v1.1.0 +
- v1.0.0 +
+
+Try out the inference widget here: https://huggingface.co/google/vit-base-patch16-224
+
+Content:
+- [PyTorch version, Trainer](#pytorch-version-trainer)
+- [PyTorch version, no Trainer](#pytorch-version-no-trainer)
+
+## PyTorch version, Trainer
+
+Based on the script [`run_image_classification.py`](https://github.com/huggingface/transformers/blob/main/examples/pytorch/image-classification/run_image_classification.py).
+
+The script leverages the 🤗 [Trainer API](https://huggingface.co/docs/transformers/main_classes/trainer) to automatically take care of the training for you, running on distributed environments right away.
+
+### Using datasets from Hub
+
+Here we show how to fine-tune a Vision Transformer (`ViT`) on the [beans](https://huggingface.co/datasets/beans) dataset, to classify the disease type of bean leaves.
+
+```bash
+python run_image_classification.py \
+ --dataset_name beans \
+ --output_dir ./beans_outputs/ \
+ --remove_unused_columns False \
+ --label_column_name labels \
+ --do_train \
+ --do_eval \
+ --push_to_hub \
+ --push_to_hub_model_id vit-base-beans \
+ --learning_rate 2e-5 \
+ --num_train_epochs 5 \
+ --per_device_train_batch_size 8 \
+ --per_device_eval_batch_size 8 \
+ --logging_strategy steps \
+ --logging_steps 10 \
+ --eval_strategy epoch \
+ --save_strategy epoch \
+ --load_best_model_at_end True \
+ --save_total_limit 3 \
+ --seed 1337
+```
+
+👀 See the results here: [nateraw/vit-base-beans](https://huggingface.co/nateraw/vit-base-beans).
+
+Note that you can replace the model and dataset by simply setting the `model_name_or_path` and `dataset_name` arguments respectively, with any model or dataset from the [hub](https://huggingface.co/). For an overview of all possible arguments, we refer to the [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) of the `TrainingArguments`, which can be passed as flags.
+
+> If your model classification head dimensions do not fit the number of labels in the dataset, you can specify `--ignore_mismatched_sizes` to adapt it.
+
+### Using your own data
+
+To use your own dataset, there are 2 ways:
+- you can either provide your own folders as `--train_dir` and/or `--validation_dir` arguments
+- you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the `--dataset_name` argument.
+
+Below, we explain both in more detail.
+
+#### Provide them as folders
+
+If you provide your own folders with images, the script expects the following directory structure:
+
+```bash
+root/dog/xxx.png
+root/dog/xxy.png
+root/dog/[...]/xxz.png
+
+root/cat/123.png
+root/cat/nsdf3.png
+root/cat/[...]/asd932_.png
+```
+
+In other words, you need to organize your images in subfolders, based on their class. You can then run the script like this:
+
+```bash
+python run_image_classification.py \
+ --train_dir 
+
+ SimMIM framework. Taken from the original paper.
+
+The goal for the model is to predict raw pixel values for the masked patches, using just a linear layer as prediction head. The model is trained using a simple L1 loss.
+
+### Using datasets from 🤗 datasets
+
+Here we show how to pre-train a `ViT` from scratch for masked image modeling on the [cifar10](https://huggingface.co/datasets/cifar10) dataset.
+
+Alternatively, one can decide to further pre-train an already pre-trained (or fine-tuned) checkpoint from the [hub](https://huggingface.co/). This can be done by setting the `model_name_or_path` argument to "google/vit-base-patch16-224-in21k" for example (and not specifying the `model_type` argument).
+
+```bash
+!python run_mim.py \
+ --model_type vit \
+ --output_dir ./outputs/ \
+ --overwrite_output_dir \
+ --remove_unused_columns False \
+ --label_names bool_masked_pos \
+ --do_train \
+ --do_eval \
+ --learning_rate 2e-5 \
+ --weight_decay 0.05 \
+ --num_train_epochs 100 \
+ --per_device_train_batch_size 8 \
+ --per_device_eval_batch_size 8 \
+ --logging_strategy steps \
+ --logging_steps 10 \
+ --eval_strategy epoch \
+ --save_strategy epoch \
+ --load_best_model_at_end True \
+ --save_total_limit 3 \
+ --seed 1337
+```
+
+Here, we train for 100 epochs with a learning rate of 2e-5. Note that the SimMIM authors used a more sophisticated learning rate schedule, see the [config files](https://github.com/microsoft/SimMIM/blob/main/configs/vit_base__800ep/simmim_pretrain__vit_base__img224__800ep.yaml) for more info. One can easily tweak the script to include this learning rate schedule (several learning rate schedulers are supported via the [training arguments](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments)).
+
+We can also for instance replicate the pre-training of a Swin Transformer using the same architecture as used by the SimMIM authors. For this, we first create a custom configuration and save it locally:
+
+```python
+from transformers import SwinConfig
+
+IMAGE_SIZE = 192
+PATCH_SIZE = 4
+EMBED_DIM = 128
+DEPTHS = [2, 2, 18, 2]
+NUM_HEADS = [4, 8, 16, 32]
+WINDOW_SIZE = 6
+
+config = SwinConfig(
+ image_size=IMAGE_SIZE,
+ patch_size=PATCH_SIZE,
+ embed_dim=EMBED_DIM,
+ depths=DEPTHS,
+ num_heads=NUM_HEADS,
+ window_size=WINDOW_SIZE,
+)
+config.save_pretrained("path_to_config")
+```
+
+Next, we can run the script by providing the path to this custom configuration (replace `path_to_config` below with your path):
+
+```bash
+!python run_mim.py \
+ --config_name_or_path path_to_config \
+ --model_type swin \
+ --output_dir ./outputs/ \
+ --overwrite_output_dir \
+ --remove_unused_columns False \
+ --label_names bool_masked_pos \
+ --do_train \
+ --do_eval \
+ --learning_rate 2e-5 \
+ --num_train_epochs 5 \
+ --per_device_train_batch_size 8 \
+ --per_device_eval_batch_size 8 \
+ --logging_strategy steps \
+ --logging_steps 10 \
+ --eval_strategy epoch \
+ --save_strategy epoch \
+ --load_best_model_at_end True \
+ --save_total_limit 3 \
+ --seed 1337
+```
+
+This will train a Swin Transformer from scratch.
+
+### Using your own data
+
+To use your own dataset, the training script expects the following directory structure:
+
+```bash
+root/dog/xxx.png
+root/dog/xxy.png
+root/dog/[...]/xxz.png
+
+root/cat/123.png
+root/cat/nsdf3.png
+root/cat/[...]/asd932_.png
+```
+
+Note that you can put images in dummy subfolders, whose names will be ignored by default (as labels aren't required). You can also just place all images into a single dummy subfolder. Once you've prepared your dataset, you can run the script like this:
+
+```bash
+python run_mim.py \
+ --model_type vit \
+ --dataset_name nateraw/image-folder \
+ --train_dir 
+
+ Original hyperparameters. Taken from the original paper.
+
+Alternatively, one can decide to further pre-train an already pre-trained (or fine-tuned) checkpoint from the [hub](https://huggingface.co/). This can be done by setting the `model_name_or_path` argument to "facebook/vit-mae-base" for example.
+
+
+### Using your own data
+
+To use your own dataset, the training script expects the following directory structure:
+
+```bash
+root/dog/xxx.png
+root/dog/xxy.png
+root/dog/[...]/xxz.png
+
+root/cat/123.png
+root/cat/nsdf3.png
+root/cat/[...]/asd932_.png
+```
+
+Note that you can put images in dummy subfolders, whose names will be ignored by default (as labels aren't required). You can also just place all images into a single dummy subfolder. Once you've prepared your dataset, you can run the script like this:
+
+```bash
+python run_mae.py \
+ --model_type vit_mae \
+ --dataset_name nateraw/image-folder \
+ --train_dir
+
+Try out the inference widget here: https://huggingface.co/google/vit-base-patch16-224
+
+## TensorFlow
+
+Based on the script [`run_image_classification.py`](https://github.com/huggingface/transformers/blob/main/examples/tensorflow/image-classification/run_image_classification.py).
+
+### Using datasets from Hub
+
+Here we show how to fine-tune a Vision Transformer (`ViT`) on the [beans](https://huggingface.co/datasets/beans) dataset, to classify the disease type of bean leaves. The following will train a model and push it to the `amyeroberts/vit-base-beans` repo.
+
+```bash
+python run_image_classification.py \
+ --dataset_name beans \
+ --output_dir ./beans_outputs/ \
+ --remove_unused_columns False \
+ --do_train \
+ --do_eval \
+ --push_to_hub \
+ --hub_model_id amyeroberts/vit-base-beans \
+ --learning_rate 2e-5 \
+ --num_train_epochs 5 \
+ --per_device_train_batch_size 8 \
+ --per_device_eval_batch_size 8 \
+ --logging_strategy steps \
+ --logging_steps 10 \
+ --eval_strategy epoch \
+ --save_strategy epoch \
+ --load_best_model_at_end True \
+ --save_total_limit 3 \
+ --seed 1337
+```
+
+👀 See the results here: [amyeroberts/vit-base-beans](https://huggingface.co/amyeroberts/vit-base-beans).
+
+Note that you can replace the model and dataset by simply setting the `model_name_or_path` and `dataset_name` arguments respectively, with any model or dataset from the [hub](https://huggingface.co/). For an overview of all possible arguments, we refer to the [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) of the `TrainingArguments`, which can be passed as flags.
+
+> If your model classification head dimensions do not fit the number of labels in the dataset, you can specify `--ignore_mismatched_sizes` to adapt it.
+
+### Using your own data
+
+To use your own dataset, there are 2 ways:
+- you can either provide your own folders as `--train_dir` and/or `--validation_dir` arguments
+- you can upload your dataset to the hub (possibly as a private repo, if you prefer so), and simply pass the `--dataset_name` argument.
+
+Below, we explain both in more detail.
+
+#### Provide them as folders
+
+If you provide your own folders with images, the script expects the following directory structure:
+
+```bash
+root/dog/xxx.png
+root/dog/xxy.png
+root/dog/[...]/xxz.png
+
+root/cat/123.png
+root/cat/nsdf3.png
+root/cat/[...]/asd932_.png
+```
+
+In other words, you need to organize your images in subfolders, based on their class. You can then run the script like this:
+
+```bash
+python run_image_classification.py \
+ --train_dir
+ 
+
+
+
+ 
+ 
+ 
+ 
+ 
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
أحدث تقنيات التعلم الآلي لـ JAX وPyTorch وTensorFlow
+ + +
+ 
+
+
+يوفر 🤗 Transformers آلاف النماذج المُدربة مسبقًا لأداء المهام على طرائق مختلفة مثل النص والصورة والصوت.
+
+يمكن تطبيق هذه النماذج على:
+
+* 📝 النص، لمهام مثل تصنيف النص واستخراج المعلومات والرد على الأسئلة والتلخيص والترجمة وتوليد النص، في أكثر من 100 لغة.
+* 🖼️ الصور، لمهام مثل تصنيف الصور وكشف الأشياء والتجزئة.
+* 🗣️ الصوت، لمهام مثل التعرف على الكلام وتصنيف الصوت.
+
+يمكن لنماذج المحول أيضًا أداء مهام على **طرائق متعددة مجتمعة**، مثل الرد على الأسئلة الجدولية والتعرف البصري على الحروف واستخراج المعلومات من المستندات الممسوحة ضوئيًا وتصنيف الفيديو والرد على الأسئلة المرئية.
+
+يوفر 🤗 Transformers واجهات برمجة التطبيقات (APIs) لتحميل تلك النماذج المُدربة مسبقًا واستخدامها على نص معين، وضبطها بدقة على مجموعات البيانات الخاصة بك، ثم مشاركتها مع المجتمع على [مركز النماذج](https://huggingface.co/models) الخاص بنا. وفي الوقت نفسه، فإن كل وحدة نمطية Python التي تحدد بنية هي وحدة مستقلة تمامًا ويمكن تعديلها لتمكين تجارب البحث السريعة.
+
+يتم دعم 🤗 Transformers بواسطة مكتبات التعلم العميق الثلاث الأكثر شيوعًا - [Jax](https://jax.readthedocs.io/en/latest/) و [PyTorch](https://pytorch.org/) و [TensorFlow](https://www.tensorflow.org/) - مع تكامل سلس بينها. من السهل تدريب نماذجك باستخدام واحدة قبل تحميلها للاستنتاج باستخدام الأخرى.
+
+## العروض التوضيحية عبر الإنترنت
+
+يمكنك اختبار معظم نماذجنا مباشرة على صفحاتها من [مركز النماذج](https://huggingface.co/models). كما نقدم [استضافة النماذج الخاصة وإصداراتها وواجهة برمجة تطبيقات الاستدلال](https://huggingface.co/pricing) للنماذج العامة والخاصة.
+
+فيما يلي بعض الأمثلة:
+
+في معالجة اللغات الطبيعية:
+- [استكمال الكلمات المقنعة باستخدام BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [التعرف على الكيانات المسماة باستخدام إليكترا](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [توليد النص باستخدام ميسترال](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+- [الاستدلال اللغوي الطبيعي باستخدام RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [التلخيص باستخدام BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [الرد على الأسئلة باستخدام DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [الترجمة باستخدام T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+في رؤية الكمبيوتر:
+- [تصنيف الصور باستخدام ViT](https://huggingface.co/google/vit-base-patch16-224)
+- [كشف الأشياء باستخدام DETR](https://huggingface.co/facebook/detr-resnet-50)
+- [التجزئة الدلالية باستخدام SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [التجزئة الشاملة باستخدام Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic)
+- [تقدير العمق باستخدام Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything)
+- [تصنيف الفيديو باستخدام VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
+- [التجزئة الشاملة باستخدام OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+في الصوت:
+- [الاعتراف التلقائي بالكلام مع Whisper](https://huggingface.co/openai/whisper-large-v3)
+- [اكتشاف الكلمات الرئيسية باستخدام Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [تصنيف الصوت باستخدام محول طيف الصوت](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+
+في المهام متعددة الطرائق:
+- [الرد على الأسئلة الجدولية باستخدام TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
+- [الرد على الأسئلة المرئية باستخدام ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [وصف الصورة باستخدام LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf)
+- [تصنيف الصور بدون تدريب باستخدام SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384)
+- [الرد على أسئلة المستندات باستخدام LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
+- [تصنيف الفيديو بدون تدريب باستخدام X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
+- [كشف الأشياء بدون تدريب باستخدام OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2)
+- [تجزئة الصور بدون تدريب باستخدام CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)
+- [توليد الأقنعة التلقائي باستخدام SAM](https://huggingface.co/docs/transformers/model_doc/sam)
+
+
+## 100 مشروع يستخدم المحولات
+
+🤗 Transformers هو أكثر من مجرد مجموعة أدوات لاستخدام النماذج المُدربة مسبقًا: إنه مجتمع من المشاريع المبنية حوله ومركز Hugging Face. نريد أن يمكّن 🤗 Transformers المطورين والباحثين والطلاب والأساتذة والمهندسين وأي شخص آخر من بناء مشاريعهم التي يحلمون بها.
+
+للاحتفال بالـ 100,000 نجمة من النماذج المحولة، قررنا تسليط الضوء على المجتمع، وقد أنشأنا صفحة [awesome-transformers](./awesome-transformers.md) التي تُدرج 100 مشروعًا رائعًا تم بناؤها بالقرب من النماذج المحولة.
+
+إذا كنت تمتلك أو تستخدم مشروعًا تعتقد أنه يجب أن يكون جزءًا من القائمة، فالرجاء فتح PR لإضافته!
+
+## إذا كنت تبحث عن دعم مخصص من فريق Hugging Face
+
+
+
++ +## جولة سريعة + +لاستخدام نموذج على الفور على إدخال معين (نص أو صورة أو صوت، ...)، نوفر واجهة برمجة التطبيقات (API) الخاصة بـ `pipeline`. تجمع خطوط الأنابيب بين نموذج مُدرب مسبقًا ومعالجة ما قبل التدريب التي تم استخدامها أثناء تدريب هذا النموذج. فيما يلي كيفية استخدام خط أنابيب بسرعة لتصنيف النصوص الإيجابية مقابل السلبية: + +```python +>>> from transformers import pipeline + +# خصص خط أنابيب للتحليل الشعوري +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +يسمح السطر الثاني من التعليمات البرمجية بتحميل النموذج المُدرب مسبقًا الذي يستخدمه خط الأنابيب وتخزينه مؤقتًا، بينما يقوم السطر الثالث بتقييمه على النص المحدد. هنا، تكون الإجابة "إيجابية" بثقة تبلغ 99.97%. + +تتوفر العديد من المهام على خط أنابيب مُدرب مسبقًا جاهز للاستخدام، في NLP ولكن أيضًا في رؤية الكمبيوتر والخطاب. على سبيل المثال، يمكننا بسهولة استخراج الأشياء المكتشفة في صورة: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# قم بتنزيل صورة بها قطط لطيفة +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# خصص خط أنابيب لكشف الأشياء +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621، + 'label': 'remote'، + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}، + {'score': 0.9960021376609802، + 'label': 'remote'، + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}، + {'score': 0.9954745173454285، + 'label': 'couch'، + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}، + {'score': 0.9988006353378296، + 'label': 'cat'، + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}، + {'score': 0.9986783862113953، + 'label': 'cat'، + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +هنا، نحصل على قائمة بالأشياء المكتشفة في الصورة، مع مربع يحيط بالشيء وتقييم الثقة. فيما يلي الصورة الأصلية على اليسار، مع عرض التوقعات على اليمين: + +
+ 
+ 
+
+
+يمكنك معرفة المزيد حول المهام التي تدعمها واجهة برمجة التطبيقات (API) الخاصة بـ `pipeline` في [هذا البرنامج التعليمي](https://huggingface.co/docs/transformers/task_summary).
+
+بالإضافة إلى `pipeline`، لاستخدام أي من النماذج المُدربة مسبقًا على مهمتك، كل ما عليك هو ثلاثة أسطر من التعليمات البرمجية. فيما يلي إصدار PyTorch:
+```python
+>>> from transformers import AutoTokenizer، AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!"، return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+
+وهنا رمز مماثل لـ TensorFlow:
+```python
+>>> from transformers import AutoTokenizer، TFAutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!"، return_tensors="tf")
+>>> outputs = model(**inputs)
+```
+
+المُعلم مسؤول عن جميع المعالجة المسبقة التي يتوقعها النموذج المُدرب مسبقًا ويمكن استدعاؤه مباشرة على سلسلة واحدة (كما هو موضح في الأمثلة أعلاه) أو قائمة. سيقوم بإخراج قاموس يمكنك استخدامه في التعليمات البرمجية لأسفل أو تمريره مباشرة إلى نموذجك باستخدام عامل فك التعبئة **.
+
+النموذج نفسه هو وحدة نمطية عادية [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) أو [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (حسب backend) والتي يمكنك استخدامها كالمعتاد. [يوضح هذا البرنامج التعليمي](https://huggingface.co/docs/transformers/training) كيفية دمج مثل هذا النموذج في حلقة تدريب PyTorch أو TensorFlow التقليدية، أو كيفية استخدام واجهة برمجة تطبيقات `Trainer` لدينا لضبطها بدقة بسرعة على مجموعة بيانات جديدة.
+
+## لماذا يجب أن أستخدم المحولات؟
+
+1. نماذج سهلة الاستخدام وحديثة:
+ - أداء عالي في فهم اللغة الطبيعية وتوليدها ورؤية الكمبيوتر والمهام الصوتية.
+ - حاجز دخول منخفض للمربين والممارسين.
+ - عدد قليل من التجريدات التي يواجهها المستخدم مع ثلاث فئات فقط للتعلم.
+ - واجهة برمجة تطبيقات (API) موحدة لاستخدام جميع نماذجنا المُدربة مسبقًا.
+
+1. تكاليف الكمبيوتر أقل، وبصمة كربونية أصغر:
+ - يمكن للباحثين مشاركة النماذج المدربة بدلاً من إعادة التدريب دائمًا.
+ - يمكن للممارسين تقليل وقت الكمبيوتر وتكاليف الإنتاج.
+ - عشرات البنيات مع أكثر من 400,000 نموذج مُدرب مسبقًا عبر جميع الطرائق.
+
+1. اختر الإطار المناسب لكل جزء من عمر النموذج:
+ - تدريب النماذج الحديثة في 3 أسطر من التعليمات البرمجية.
+ - قم بنقل نموذج واحد بين إطارات TF2.0/PyTorch/JAX حسب الرغبة.
+ - اختر الإطار المناسب بسلاسة للتدريب والتقييم والإنتاج.
+
+1. قم بسهولة بتخصيص نموذج أو مثال وفقًا لاحتياجاتك:
+ - نوفر أمثلة لكل بنية لإعادة إنتاج النتائج التي نشرها مؤلفوها الأصليون.
+ - يتم عرض داخليات النموذج بشكل متسق قدر الإمكان.
+ - يمكن استخدام ملفات النموذج بشكل مستقل عن المكتبة للتجارب السريعة.
+
+## لماذا لا يجب أن أستخدم المحولات؟
+
+- ليست هذه المكتبة عبارة عن مجموعة أدوات من الصناديق المكونة للشبكات العصبية. لم يتم إعادة صياغة التعليمات البرمجية في ملفات النموذج باستخدام تجريدات إضافية عن قصد، بحيث يمكن للباحثين إجراء حلقات تكرار سريعة على كل من النماذج دون الغوص في تجريدات/ملفات إضافية.
+- لا يُقصد بواجهة برمجة التطبيقات (API) للتدريب العمل على أي نموذج ولكنه مُستَهدف للعمل مع النماذج التي توفرها المكتبة. للحلقات العامة للتعلم الآلي، يجب استخدام مكتبة أخرى (ربما، [تسريع](https://huggingface.co/docs/accelerate)).
+- في حين أننا نسعى جاهدين لتقديم أكبر عدد ممكن من حالات الاستخدام، فإن البرامج النصية الموجودة في مجلد [الأمثلة](https://github.com/huggingface/transformers/tree/main/examples) الخاص بنا هي مجرد أمثلة. من المتوقع ألا تعمل هذه البرامج النصية خارج الصندوق على مشكلتك المحددة وأنه سيُطلب منك تغيير بضع أسطر من التعليمات البرمجية لتكييفها مع احتياجاتك.
+
+## التثبيت
+
+### باستخدام pip
+
+تم اختبار هذا المستودع على Python 3.9+، Flax 0.4.1+، PyTorch 2.1+، و TensorFlow 2.6+.
+
+يجب تثبيت 🤗 Transformers في [بيئة افتراضية](https://docs.python.org/3/library/venv.html). إذا كنت غير معتاد على البيئات الافتراضية Python، فراجع [دليل المستخدم](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+أولاً، قم بإنشاء بيئة افتراضية بالإصدار Python الذي تنوي استخدامه وقم بتنشيطه.
+
+بعد ذلك، ستحتاج إلى تثبيت واحدة على الأقل من Flax أو PyTorch أو TensorFlow.
+يرجى الرجوع إلى [صفحة تثبيت TensorFlow](https://www.tensorflow.org/install/)، و [صفحة تثبيت PyTorch](https://pytorch.org/get-started/locally/#start-locally) و/أو [صفحة تثبيت Flax](https://github.com/google/flax#quick-install) و [صفحة تثبيت Jax](https://github.com/google/jax#installation) بشأن أمر التثبيت المحدد لمنصتك.
+
+عندما يتم تثبيت إحدى هذه المكتبات الخلفية، يمكن تثبيت 🤗 Transformers باستخدام pip كما يلي:
+
+```bash
+pip install transformers
+```
+
+إذا كنت ترغب في اللعب مع الأمثلة أو تحتاج إلى أحدث إصدار من التعليمات البرمجية ولا يمكنك الانتظار حتى يتم إصدار إصدار جديد، فيجب [تثبيت المكتبة من المصدر](https://huggingface.co/docs/transformers/installation#installing-from-source).
+
+### باستخدام conda
+
+يمكن تثبيت 🤗 Transformers باستخدام conda كما يلي:
+
+```shell script
+conda install conda-forge::transformers
+```
+
+> **_ملاحظة:_** تم إيقاف تثبيت `transformers` من قناة `huggingface`.
+
+اتبع صفحات التثبيت الخاصة بـ Flax أو PyTorch أو TensorFlow لمعرفة كيفية تثبيتها باستخدام conda.
+
+> **_ملاحظة:_** على Windows، قد تتم مطالبتك بتنشيط وضع المطور للاستفادة من التخزين المؤقت. إذا لم يكن هذا خيارًا بالنسبة لك، فيرجى إعلامنا بذلك في [هذه المشكلة](https://github.com/huggingface/huggingface_hub/issues/1062).
+
+## بنيات النماذج
+
+**[جميع نقاط تفتيش النموذج](https://huggingface.co/models)** التي يوفرها 🤗 Transformers مدمجة بسلاسة من مركز [huggingface.co](https://huggingface.co/models) [model hub](https://huggingface.co/models)، حيث يتم تحميلها مباشرة من قبل [المستخدمين](https://huggingface.co/users) و [المنظمات](https://huggingface.co/organizations).
+
+عدد نقاط التفتيش الحالية: 
+
+يوفر 🤗 Transformers حاليًا البنيات التالية: راجع [هنا](https://huggingface.co/docs/transformers/model_summary) للحصول على ملخص لكل منها.
+
+للتحقق مما إذا كان لكل نموذج تنفيذ في Flax أو PyTorch أو TensorFlow، أو كان لديه مُعلم مرفق مدعوم من مكتبة 🤗 Tokenizers، يرجى الرجوع إلى [هذا الجدول](https://huggingface.co/docs/transformers/index#supported-frameworks).
+
+تم اختبار هذه التطبيقات على العديد من مجموعات البيانات (راجع البرامج النصية المثالية) ويجب أن تتطابق مع أداء التنفيذ الأصلي. يمكنك العثور على مزيد من التفاصيل حول الأداء في قسم الأمثلة من [الوثائق](https://github.com/huggingface/transformers/tree/main/examples).
+
+
+## تعلم المزيد
+
+| القسم | الوصف |
+|-|-|
+| [وثائق](https://huggingface.co/docs/transformers/) | وثائق واجهة برمجة التطبيقات (API) الكاملة والبرامج التعليمية |
+| [ملخص المهام](https://huggingface.co/docs/transformers/task_summary) | المهام التي يدعمها 🤗 Transformers |
+| [برنامج تعليمي لمعالجة مسبقة](https://huggingface.co/docs/transformers/preprocessing) | استخدام فئة `Tokenizer` لإعداد البيانات للنماذج |
+| [التدريب والضبط الدقيق](https://huggingface.co/docs/transformers/training) | استخدام النماذج التي يوفرها 🤗 Transformers في حلقة تدريب PyTorch/TensorFlow وواجهة برمجة تطبيقات `Trainer` |
+| [جولة سريعة: البرامج النصية للضبط الدقيق/الاستخدام](https://github.com/huggingface/transformers/tree/main/examples) | البرامج النصية المثالية للضبط الدقيق للنماذج على مجموعة واسعة من المهام |
+| [مشاركة النماذج وتحميلها](https://huggingface.co/docs/transformers/model_sharing) | تحميل ومشاركة نماذجك المضبوطة بدقة مع المجتمع |
+
+## الاستشهاد
+
+لدينا الآن [ورقة](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) يمكنك الاستشهاد بها لمكتبة 🤗 Transformers:
+```bibtex
+@inproceedings{wolf-etal-2020-transformers،
+ title = "Transformers: State-of-the-Art Natural Language Processing"،
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and R{\'e}mi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush"،
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations"،
+ month = oct،
+ year = "2020"،
+ address = "Online"،
+ publisher = "Association for Computational Linguistics"،
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6"،
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_de.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_de.md
new file mode 100644
index 0000000000000000000000000000000000000000..b913df894dc147d575a8d283b7eab7265306bbb2
--- /dev/null
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@@ -0,0 +1,318 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
Maschinelles Lernen auf dem neuesten Stand der Technik für JAX, PyTorch und TensorFlow
+ + +
+
+
+
+🤗 Transformers bietet Tausende von vortrainierten Modellen, um Aufgaben in verschiedenen Modalitäten wie Text, Bild und Audio durchzuführen.
+
+Diese Modelle können angewendet werden, auf:
+
+* 📝 Text - für Aufgaben wie Textklassifizierung, Informationsextraktion, Question Answering, automatische Textzusammenfassung, maschinelle Übersetzung und Textgenerierung in über 100 Sprachen.
+* 🖼️ Bilder - für Aufgaben wie Bildklassifizierung, Objekterkennung und Segmentierung.
+* 🗣️ Audio - für Aufgaben wie Spracherkennung und Audioklassifizierung.
+
+Transformer-Modelle können auch Aufgaben für **mehrere Modalitäten in Kombination** durchführen, z. B. tabellenbasiertes Question Answering, optische Zeichenerkennung, Informationsextraktion aus gescannten Dokumenten, Videoklassifizierung und visuelles Question Answering.
+
+🤗 Transformers bietet APIs, um diese vortrainierten Modelle schnell herunterzuladen und für einen gegebenen Text zu verwenden, sie auf Ihren eigenen Datensätzen zu feintunen und dann mit der Community in unserem [Model Hub](https://huggingface.co/models) zu teilen. Gleichzeitig ist jedes Python-Modul, das eine Architektur definiert, komplett eigenständig und kann modifiziert werden, um schnelle Forschungsexperimente zu ermöglichen.
+
+🤗 Transformers unterstützt die nahtlose Integration von drei der beliebtesten Deep-Learning-Bibliotheken: [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) und [TensorFlow](https://www.tensorflow.org/). Trainieren Sie Ihr Modell in einem Framework und laden Sie es zur Inferenz unkompliziert mit einem anderen.
+
+## Online-Demos
+
+Sie können die meisten unserer Modelle direkt auf ihren Seiten im [Model Hub](https://huggingface.co/models) testen. Wir bieten auch [privates Modell-Hosting, Versionierung, & eine Inferenz-API](https://huggingface.co/pricing) für öffentliche und private Modelle an.
+
+Hier sind einige Beispiele:
+
+In der Computerlinguistik:
+
+- [Maskierte Wortvervollständigung mit BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Eigennamenerkennung mit Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [Textgenerierung mit GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
+- [Natural Language Inference mit RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [Automatische Textzusammenfassung mit BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [Question Answering mit DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [Maschinelle Übersetzung mit T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+In der Computer Vision:
+
+- [Bildklassifizierung mit ViT](https://huggingface.co/google/vit-base-patch16-224)
+- [Objekterkennung mit DETR](https://huggingface.co/facebook/detr-resnet-50)
+- [Semantische Segmentierung mit SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [Panoptische Segmentierung mit MaskFormer](https://huggingface.co/facebook/maskformer-swin-small-coco)
+- [Depth Estimation mit DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
+- [Videoklassifizierung mit VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
+- [Universelle Segmentierung mit OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+Im Audio-Bereich:
+
+- [Automatische Spracherkennung mit Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)
+- [Keyword Spotting mit Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [Audioklassifizierung mit Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+
+In multimodalen Aufgaben:
+
+- [Tabellenbasiertes Question Answering mit TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
+- [Visuelles Question Answering mit ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [Zero-Shot-Bildklassifizierung mit CLIP](https://huggingface.co/openai/clip-vit-large-patch14)
+- [Dokumentenbasiertes Question Answering mit LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
+- [Zero-Shot-Videoklassifizierung mit X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
+
+## 100 Projekte, die 🤗 Transformers verwenden
+
+🤗 Transformers ist mehr als nur ein Toolkit zur Verwendung von vortrainierten Modellen: Es ist eine Gemeinschaft von Projekten, die darum herum und um den Hugging Face Hub aufgebaut sind. Wir möchten, dass 🤗 Transformers es Entwicklern, Forschern, Studenten, Professoren, Ingenieuren und jedem anderen ermöglicht, ihre Traumprojekte zu realisieren.
+
+Um die 100.000 Sterne von 🤗 Transformers zu feiern, haben wir beschlossen, die Gemeinschaft in den Mittelpunkt zu stellen und die Seite [awesome-transformers](./awesome-transformers.md) erstellt, die 100 unglaubliche Projekte auflistet, die zusammen mit 🤗 Transformers realisiert wurden.
+
+Wenn Sie ein Projekt besitzen oder nutzen, von dem Sie glauben, dass es Teil der Liste sein sollte, öffnen Sie bitte einen PR, um es hinzuzufügen!
+
+## Wenn Sie individuelle Unterstützung vom Hugging Face-Team möchten
+
+
+
++ +## Schnelleinstieg + +Um sofort ein Modell mit einer bestimmten Eingabe (Text, Bild, Audio ...) zu verwenden, bieten wir die `pipeline`-API an. Pipelines kombinieren ein vortrainiertes Modell mit der jeweiligen Vorverarbeitung, die während dessen Trainings verwendet wurde. Hier sehen Sie, wie man schnell eine Pipeline verwenden kann, um positive und negative Texte zu klassifizieren: + +```python +>>> from transformers import pipeline + +# Zuweisung einer Pipeline für die Sentiment-Analyse +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +Die zweite Codezeile lädt und cacht das vortrainierte Modell, das von der Pipeline verwendet wird, während die dritte es an dem gegebenen Text evaluiert. Hier ist die Antwort "positiv" mit einer Konfidenz von 99,97 %. + +Viele Aufgaben, sowohl in der Computerlinguistik als auch in der Computer Vision und Sprachverarbeitung, haben eine vortrainierte `pipeline`, die sofort einsatzbereit ist. Z. B. können wir leicht erkannte Objekte in einem Bild extrahieren: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Download eines Bildes mit süßen Katzen +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Zuweisung einer Pipeline für die Objekterkennung +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +Hier erhalten wir eine Liste von Objekten, die im Bild erkannt wurden, mit einer Markierung, die das Objekt eingrenzt, und einem zugehörigen Konfidenzwert. Folgend ist das Originalbild links und die Vorhersagen rechts dargestellt: + +
+
+
+
+
+Sie können mehr über die von der `pipeline`-API unterstützten Aufgaben in [diesem Tutorial](https://huggingface.co/docs/transformers/task_summary) erfahren.
+
+Zusätzlich zur `pipeline` benötigt es nur drei Zeilen Code, um eines der vortrainierten Modelle für Ihre Aufgabe herunterzuladen und zu verwenden. Hier ist der Code für die PyTorch-Version:
+
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+
+Und hier ist der entsprechende Code für TensorFlow:
+
+```python
+>>> from transformers import AutoTokenizer, TFAutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="tf")
+>>> outputs = model(**inputs)
+```
+
+Der Tokenizer ist für die gesamte Vorverarbeitung, die das vortrainierte Modell benötigt, verantwortlich und kann direkt auf einem einzelnen String (wie in den obigen Beispielen) oder einer Liste ausgeführt werden. Er gibt ein Dictionary aus, das Sie im darauffolgenden Code verwenden oder einfach direkt Ihrem Modell übergeben können, indem Sie den ** Operator zum Entpacken von Argumenten einsetzen.
+
+Das Modell selbst ist ein reguläres [PyTorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) oder ein [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (abhängig von Ihrem Backend), das Sie wie gewohnt verwenden können. [Dieses Tutorial](https://huggingface.co/docs/transformers/training) erklärt, wie man ein solches Modell in eine klassische PyTorch- oder TensorFlow-Trainingsschleife integrieren kann oder wie man unsere `Trainer`-API verwendet, um es schnell auf einem neuen Datensatz zu feintunen.
+
+## Warum sollten Sie 🤗 Transformers verwenden?
+
+1. Benutzerfreundliche Modelle auf dem neuesten Stand der Technik:
+ - Hohe Leistung bei Aufgaben zu Natural Language Understanding & Generation, Computer Vision und Audio.
+ - Niedrige Einstiegshürde für Bildungskräfte und Praktiker.
+ - Wenige benutzerseitige Abstraktionen mit nur drei zu lernenden Klassen.
+ - Eine einheitliche API für die Verwendung aller unserer vortrainierten Modelle.
+
+1. Geringere Rechenkosten, kleinerer CO2-Fußabdruck:
+ - Forscher können trainierte Modelle teilen, anstatt sie immer wieder neu zu trainieren.
+ - Praktiker können die Rechenzeit und Produktionskosten reduzieren.
+ - Dutzende Architekturen mit über 400.000 vortrainierten Modellen über alle Modalitäten hinweg.
+
+1. Wählen Sie das richtige Framework für jeden Lebensabschnitt eines Modells:
+ - Trainieren Sie Modelle auf neustem Stand der Technik in nur drei Codezeilen.
+ - Verwenden Sie ein einzelnes Modell nach Belieben mit TF2.0-/PyTorch-/JAX-Frameworks.
+ - Wählen Sie nahtlos das richtige Framework für Training, Evaluation und Produktiveinsatz.
+
+1. Passen Sie ein Modell oder Beispiel leicht an Ihre Bedürfnisse an:
+ - Wir bieten Beispiele für jede Architektur an, um die von ihren ursprünglichen Autoren veröffentlichten Ergebnisse zu reproduzieren.
+ - Modellinterna sind so einheitlich wie möglich verfügbar gemacht.
+ - Modelldateien können unabhängig von der Bibliothek für schnelle Experimente verwendet werden.
+
+## Warum sollten Sie 🤗 Transformers nicht verwenden?
+
+- Diese Bibliothek ist kein modularer Werkzeugkasten mit Bausteinen für neuronale Netze. Der Code in den Modelldateien ist absichtlich nicht mit zusätzlichen Abstraktionen refaktorisiert, sodass Forscher schnell mit jedem der Modelle iterieren können, ohne sich in zusätzliche Abstraktionen/Dateien vertiefen zu müssen.
+- Die Trainings-API ist nicht dafür gedacht, mit beliebigen Modellen zu funktionieren, sondern ist für die Verwendung mit den von der Bibliothek bereitgestellten Modellen optimiert. Für generische Trainingsschleifen von maschinellem Lernen sollten Sie eine andere Bibliothek verwenden (möglicherweise [Accelerate](https://huggingface.co/docs/accelerate)).
+- Auch wenn wir bestrebt sind, so viele Anwendungsfälle wie möglich zu veranschaulichen, sind die Beispielskripte in unserem [`examples`](./examples) Ordner genau das: Beispiele. Es ist davon auszugehen, dass sie nicht sofort auf Ihr spezielles Problem anwendbar sind und einige Codezeilen geändert werden müssen, um sie für Ihre Bedürfnisse anzupassen.
+
+## Installation
+
+### Mit pip
+
+Dieses Repository wurde mit Python 3.9+, Flax 0.4.1+, PyTorch 2.1+ und TensorFlow 2.6+ getestet.
+
+Sie sollten 🤗 Transformers in einer [virtuellen Umgebung](https://docs.python.org/3/library/venv.html) installieren. Wenn Sie mit virtuellen Python-Umgebungen nicht vertraut sind, schauen Sie sich den [Benutzerleitfaden](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) an.
+
+Erstellen und aktivieren Sie zuerst eine virtuelle Umgebung mit der Python-Version, die Sie verwenden möchten.
+
+Dann müssen Sie entweder Flax, PyTorch oder TensorFlow installieren. Bitte beziehe dich entsprechend auf die jeweiligen Installationsanleitungen für [TensorFlow](https://www.tensorflow.org/install/), [PyTorch](https://pytorch.org/get-started/locally/#start-locally), und/oder [Flax](https://github.com/google/flax#quick-install) und [Jax](https://github.com/google/jax#installation) für den spezifischen Installationsbefehl für Ihre Plattform.
+
+Wenn eines dieser Backends installiert ist, kann 🤗 Transformers wie folgt mit pip installiert werden:
+
+```bash
+pip install transformers
+```
+
+Wenn Sie mit den Beispielen experimentieren möchten oder die neueste Version des Codes benötigen und nicht auf eine neue Veröffentlichung warten können, müssen Sie [die Bibliothek von der Quelle installieren](https://huggingface.co/docs/transformers/installation#installing-from-source).
+
+### Mit conda
+
+🤗 Transformers kann wie folgt mit conda installiert werden:
+
+```shell script
+conda install conda-forge::transformers
+```
+
+> **_HINWEIS:_** Die Installation von `transformers` aus dem `huggingface`-Kanal ist veraltet.
+
+Folgen Sie den Installationsanleitungen von Flax, PyTorch oder TensorFlow, um zu sehen, wie sie mit conda installiert werden können.
+
+> **_HINWEIS:_** Auf Windows werden Sie möglicherweise aufgefordert, den Entwicklermodus zu aktivieren, um von Caching zu profitieren. Wenn das für Sie keine Option ist, lassen Sie es uns bitte in [diesem Issue](https://github.com/huggingface/huggingface_hub/issues/1062) wissen.
+
+## Modellarchitekturen
+
+**[Alle Modell-Checkpoints](https://huggingface.co/models)**, die von 🤗 Transformers bereitgestellt werden, sind nahtlos aus dem huggingface.co [Model Hub](https://huggingface.co/models) integriert, wo sie direkt von [Benutzern](https://huggingface.co/users) und [Organisationen](https://huggingface.co/organizations) hochgeladen werden.
+
+Aktuelle Anzahl der Checkpoints: 
+
+🤗 Transformers bietet derzeit die folgenden Architekturen an: siehe [hier](https://huggingface.co/docs/transformers/model_summary) für eine jeweilige Übersicht.
+
+Um zu überprüfen, ob jedes Modell eine Implementierung in Flax, PyTorch oder TensorFlow hat oder über einen zugehörigen Tokenizer verfügt, der von der 🤗 Tokenizers-Bibliothek unterstützt wird, schauen Sie auf [diese Tabelle](https://huggingface.co/docs/transformers/index#supported-frameworks).
+
+Diese Implementierungen wurden mit mehreren Datensätzen getestet (siehe Beispielskripte) und sollten den Leistungen der ursprünglichen Implementierungen entsprechen. Weitere Details zur Leistung finden Sie im Abschnitt der Beispiele in der [Dokumentation](https://github.com/huggingface/transformers/tree/main/examples).
+
+## Mehr erfahren
+
+| Abschnitt | Beschreibung |
+|-|-|
+| [Dokumentation](https://huggingface.co/docs/transformers/) | Vollständige API-Dokumentation und Tutorials |
+| [Zusammenfassung der Aufgaben](https://huggingface.co/docs/transformers/task_summary) | Von 🤗 Transformers unterstützte Aufgaben |
+| [Vorverarbeitungs-Tutorial](https://huggingface.co/docs/transformers/preprocessing) | Verwendung der `Tokenizer`-Klasse zur Vorverarbeitung der Daten für die Modelle |
+| [Training und Feintuning](https://huggingface.co/docs/transformers/training) | Verwendung der von 🤗 Transformers bereitgestellten Modelle in einer PyTorch-/TensorFlow-Trainingsschleife und der `Trainer`-API |
+| [Schnelleinstieg: Feintuning/Anwendungsskripte](https://github.com/huggingface/transformers/tree/main/examples) | Beispielskripte für das Feintuning von Modellen für eine breite Palette von Aufgaben |
+| [Modellfreigabe und -upload](https://huggingface.co/docs/transformers/model_sharing) | Laden Sie Ihre feingetunten Modelle hoch und teilen Sie sie mit der Community |
+
+## Zitation
+
+Wir haben jetzt ein [Paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/), das Sie für die 🤗 Transformers-Bibliothek zitieren können:
+
+```bibtex
+@inproceedings{wolf-etal-2020-transformers,
+ title = "Transformers: State-of-the-Art Natural Language Processing",
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+ month = oct,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_es.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_es.md
new file mode 100644
index 0000000000000000000000000000000000000000..d31b7f5f76c3cbc871492059687c23cd30bcb0ef
--- /dev/null
+++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_es.md
@@ -0,0 +1,301 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
Lo último de Machine Learning para JAX, PyTorch y TensorFlow
+ + +
+
+
+
+🤗 Transformers aporta miles de modelos preentrenados para realizar tareas en diferentes modalidades como texto, visión, y audio.
+
+Estos modelos pueden ser aplicados en:
+
+* 📝 Texto, para tareas como clasificación de texto, extracción de información, responder preguntas, resumir, traducir, generación de texto, en más de 100 idiomas.
+* 🖼️ Imágenes, para tareas como clasificación de imágenes, detección the objetos, y segmentación.
+* 🗣️ Audio, para tareas como reconocimiento de voz y clasificación de audio.
+
+Los modelos de Transformer también pueden realizar tareas en **muchas modalidades combinadas**, como responder preguntas, reconocimiento de carácteres ópticos,extracción de información de documentos escaneados, clasificación de video, y respuesta de preguntas visuales.
+
+🤗 Transformers aporta APIs para descargar rápidamente y usar estos modelos preentrenados en un texto dado, afinarlos en tus propios sets de datos y compartirlos con la comunidad en nuestro [centro de modelos](https://huggingface.co/models). Al mismo tiempo, cada módulo de Python que define una arquitectura es completamente independiente y se puede modificar para permitir experimentos de investigación rápidos.
+
+🤗 Transformers está respaldado por las tres bibliotecas de deep learning más populares — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) y [TensorFlow](https://www.tensorflow.org/) — con una perfecta integración entre ellos. Es sencillo entrenar sus modelos con uno antes de cargarlos para la inferencia con el otro.
+
+## Demostraciones en línea
+
+Puedes probar la mayoría de nuestros modelos directamente en sus páginas desde el [centro de modelos](https://huggingface.co/models). También ofrecemos [alojamiento de modelos privados, control de versiones y una API de inferencia](https://huggingface.co/pricing) para modelos públicos y privados.
+
+Aquí hay algunos ejemplos:
+
+En procesamiento del lenguaje natural:
+- [Terminación de palabras enmascaradas con BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Reconocimiento del nombre de la entidad con Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [Generación de texto con GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
+- [Inferencia del lenguaje natural con RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [Resumen con BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [Responder a preguntas con DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [Traducción con T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+En visión de ordenador:
+- [Clasificación de imágenes con ViT](https://huggingface.co/google/vit-base-patch16-224)
+- [Detección de objetos con DETR](https://huggingface.co/facebook/detr-resnet-50)
+- [Segmentación semántica con SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [Segmentación panóptica con DETR](https://huggingface.co/facebook/detr-resnet-50-panoptic)
+- [Segmentación Universal con OneFormer (Segmentación Semántica, de Instancia y Panóptica con un solo modelo)](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+En Audio:
+- [Reconocimiento de voz automático con Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)
+- [Detección de palabras clave con Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+
+En tareas multimodales:
+- [Respuesta visual a preguntas con ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+
+**[Escribe con Transformer](https://transformer.huggingface.co)**, construido por el equipo de Hugging Face, es la demostración oficial de las capacidades de generación de texto de este repositorio.
+
+## Si está buscando soporte personalizado del equipo de Hugging Face
+
+
+
++ +## Tour rápido + +Para usar inmediatamente un modelo en una entrada determinada (texto, imagen, audio, ...), proporcionamos la API de `pipeline`. Los pipelines agrupan un modelo previamente entrenado con el preprocesamiento que se usó durante el entrenamiento de ese modelo. Aquí se explica cómo usar rápidamente un pipeline para clasificar textos positivos frente a negativos: + +```python +>>> from transformers import pipeline + +# Allocate a pipeline for sentiment-analysis +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +La segunda línea de código descarga y almacena en caché el modelo previamente entrenado que usa la canalización, mientras que la tercera lo evalúa en el texto dado. Aquí la respuesta es "positiva" con una confianza del 99,97%. + +Muchas tareas tienen un `pipeline` preentrenado listo para funcionar, en NLP pero también en visión por ordenador y habla. Por ejemplo, podemos extraer fácilmente los objetos detectados en una imagen: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Download an image with cute cats +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Allocate a pipeline for object detection +>>> object_detector = pipeline('object_detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +Aquí obtenemos una lista de objetos detectados en la imagen, con un cuadro que rodea el objeto y una puntuación de confianza. Aquí está la imagen original a la derecha, con las predicciones mostradas a la izquierda: + +
+
+
+
+
+Puedes obtener más información sobre las tareas admitidas por la API de `pipeline` en [este tutorial](https://huggingface.co/docs/transformers/task_summary).
+
+Además de `pipeline`, para descargar y usar cualquiera de los modelos previamente entrenados en su tarea dada, todo lo que necesita son tres líneas de código. Aquí está la versión de PyTorch:
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+
+Y aquí está el código equivalente para TensorFlow:
+```python
+>>> from transformers import AutoTokenizer, TFAutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="tf")
+>>> outputs = model(**inputs)
+```
+
+El tokenizador es responsable de todo el preprocesamiento que espera el modelo preentrenado y se puede llamar directamente en una sola cadena (como en los ejemplos anteriores) o en una lista. Este dará como resultado un diccionario que puedes usar en el código descendente o simplemente pasarlo directamente a su modelo usando el operador de desempaquetado de argumento **.
+
+El modelo en si es un [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) normal o un [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (dependiendo De tu backend) que puedes usar de forma habitual. [Este tutorial](https://huggingface.co/docs/transformers/training) explica cómo integrar un modelo de este tipo en un ciclo de entrenamiento PyTorch o TensorFlow clásico, o como usar nuestra API `Trainer` para ajustar rápidamente un nuevo conjunto de datos.
+
+## ¿Por qué debo usar transformers?
+
+1. Modelos de última generación fáciles de usar:
+ - Alto rendimiento en comprensión y generación de lenguaje natural, visión artificial y tareas de audio.
+ - Baja barrera de entrada para educadores y profesionales.
+ - Pocas abstracciones de cara al usuario con solo tres clases para aprender.
+ - Una API unificada para usar todos nuestros modelos preentrenados.
+
+1. Menores costes de cómputo, menor huella de carbono:
+ - Los investigadores pueden compartir modelos entrenados en lugar de siempre volver a entrenar.
+ - Los profesionales pueden reducir el tiempo de cómputo y los costos de producción.
+ - Docenas de arquitecturas con más de 60 000 modelos preentrenados en todas las modalidades.
+
+1. Elija el marco adecuado para cada parte de la vida útil de un modelo:
+ - Entrene modelos de última generación en 3 líneas de código.
+ - Mueva un solo modelo entre los marcos TF2.0/PyTorch/JAX a voluntad.
+ - Elija sin problemas el marco adecuado para la formación, la evaluación y la producción.
+
+1. Personalice fácilmente un modelo o un ejemplo según sus necesidades:
+ - Proporcionamos ejemplos de cada arquitectura para reproducir los resultados publicados por sus autores originales..
+ - Los internos del modelo están expuestos lo más consistentemente posible..
+ - Los archivos modelo se pueden usar independientemente de la biblioteca para experimentos rápidos.
+
+## ¿Por qué no debería usar transformers?
+
+- Esta biblioteca no es una caja de herramientas modular de bloques de construcción para redes neuronales. El código en los archivos del modelo no se refactoriza con abstracciones adicionales a propósito, de modo que los investigadores puedan iterar rápidamente en cada uno de los modelos sin sumergirse en abstracciones/archivos adicionales.
+- La API de entrenamiento no está diseñada para funcionar en ningún modelo, pero está optimizada para funcionar con los modelos proporcionados por la biblioteca. Para bucles genéricos de aprendizaje automático, debe usar otra biblioteca (posiblemente, [Accelerate](https://huggingface.co/docs/accelerate)).
+- Si bien nos esforzamos por presentar tantos casos de uso como sea posible, los scripts en nuestra [carpeta de ejemplos](https://github.com/huggingface/transformers/tree/main/examples) son solo eso: ejemplos. Se espera que no funcionen de forma inmediata en su problema específico y que deba cambiar algunas líneas de código para adaptarlas a sus necesidades.
+
+## Instalación
+
+### Con pip
+
+Este repositorio está probado en Python 3.9+, Flax 0.4.1+, PyTorch 2.1+ y TensorFlow 2.6+.
+
+Deberías instalar 🤗 Transformers en un [entorno virtual](https://docs.python.org/3/library/venv.html). Si no estas familiarizado con los entornos virtuales de Python, consulta la [guía de usuario](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+Primero, crea un entorno virtual con la versión de Python que vas a usar y actívalo.
+
+Luego, deberás instalar al menos uno entre Flax, PyTorch o TensorFlow.
+Por favor, ve a la [página de instalación de TensorFlow](https://www.tensorflow.org/install/), [página de instalación de PyTorch](https://pytorch.org/get-started/locally/#start-locally) y/o las páginas de instalación de [Flax](https://github.com/google/flax#quick-install) y [Jax](https://github.com/google/jax#installation) con respecto al comando de instalación específico para tu plataforma.
+
+Cuando se ha instalado uno de esos backends, los 🤗 Transformers se pueden instalar usando pip de la siguiente manera:
+
+```bash
+pip install transformers
+```
+
+Si deseas jugar con los ejemplos o necesitas la última versión del código y no puedes esperar a una nueva versión, tienes que [instalar la librería de la fuente](https://huggingface.co/docs/transformers/installation#installing-from-source).
+
+### Con conda
+
+🤗 Transformers se puede instalar usando conda de la siguiente manera:
+
+```shell script
+conda install conda-forge::transformers
+```
+
+> **_NOTA:_** Instalar `transformers` desde el canal `huggingface` está obsoleto.
+
+Sigue las páginas de instalación de Flax, PyTorch o TensorFlow para ver cómo instalarlos con conda.
+
+> **_NOTA:_** En Windows, es posible que se le pida que active el modo de desarrollador para beneficiarse del almacenamiento en caché. Si esta no es una opción para usted, háganoslo saber en [esta issue](https://github.com/huggingface/huggingface_hub/issues/1062).
+
+## Arquitecturas modelo
+
+**[Todos los puntos de control del modelo](https://huggingface.co/models)** aportados por 🤗 Transformers están perfectamente integrados desde huggingface.co [Centro de modelos](https://huggingface.co) donde son subidos directamente por los [usuarios](https://huggingface.co/users) y [organizaciones](https://huggingface.co/organizations).
+
+Número actual de puntos de control: 
+
+🤗 Transformers actualmente proporciona las siguientes arquitecturas: ver [aquí](https://huggingface.co/docs/transformers/model_summary) para un resumen de alto nivel de cada uno de ellas.
+
+Para comprobar si cada modelo tiene una implementación en Flax, PyTorch o TensorFlow, o tiene un tokenizador asociado respaldado por la librería 🤗 Tokenizers, ve a [esta tabla](https://huggingface.co/docs/transformers/index#supported-frameworks).
+
+Estas implementaciones se han probado en varios conjuntos de datos (consulte los scripts de ejemplo) y deberían coincidir con el rendimiento de las implementaciones originales. Puede encontrar más detalles sobre el rendimiento en la sección Examples de la [documentación](https://github.com/huggingface/transformers/tree/main/examples).
+
+
+## Aprender más
+
+| Sección | Descripción |
+|-|-|
+| [Documentación](https://huggingface.co/docs/transformers/) | Toda la documentación de la API y tutoriales |
+| [Resumen de tareas](https://huggingface.co/docs/transformers/task_summary) | Tareas soportadas 🤗 Transformers |
+| [Tutorial de preprocesamiento](https://huggingface.co/docs/transformers/preprocessing) | Usando la clase `Tokenizer` para preparar datos para los modelos |
+| [Entrenamiento y puesta a punto](https://huggingface.co/docs/transformers/training) | Usando los modelos aportados por 🤗 Transformers en un bucle de entreno de PyTorch/TensorFlow y la API de `Trainer` |
+| [Recorrido rápido: secuencias de comandos de ajuste/uso](https://github.com/huggingface/transformers/tree/main/examples) | Scripts de ejemplo para ajustar modelos en una amplia gama de tareas |
+| [Compartir y subir modelos](https://huggingface.co/docs/transformers/model_sharing) | Carga y comparte tus modelos perfeccionados con la comunidad |
+| [Migración](https://huggingface.co/docs/transformers/migration) | Migra a 🤗 Transformers desde `pytorch-transformers` o `pytorch-pretrained-bert` |
+
+## Citación
+
+Ahora nosotros tenemos un [paper](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) que puedes citar para la librería de 🤗 Transformers:
+```bibtex
+@inproceedings{wolf-etal-2020-transformers,
+ title = "Transformers: State-of-the-Art Natural Language Processing",
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+ month = oct,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_fr.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_fr.md
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@@ -0,0 +1,315 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
Apprentissage automatique de pointe pour JAX, PyTorch et TensorFlow
+ + +
+
+
+
+🤗 Transformers fournit des milliers de modèles pré-entraînés pour effectuer des tâches sur différentes modalités telles que le texte, la vision et l'audio.
+
+Ces modèles peuvent être appliqués à :
+
+* 📝 Texte, pour des tâches telles que la classification de texte, l'extraction d'informations, la réponse aux questions, le résumé, la traduction et la génération de texte, dans plus de 100 langues.
+* 🖼️ Images, pour des tâches telles que la classification d'images, la détection d'objets et la segmentation.
+* 🗣️ Audio, pour des tâches telles que la reconnaissance vocale et la classification audio.
+
+Les modèles de transformer peuvent également effectuer des tâches sur **plusieurs modalités combinées**, telles que la réponse aux questions sur des tableaux, la reconnaissance optique de caractères, l'extraction d'informations à partir de documents numérisés, la classification vidéo et la réponse aux questions visuelles.
+
+🤗 Transformers fournit des API pour télécharger et utiliser rapidement ces modèles pré-entraînés sur un texte donné, les affiner sur vos propres ensembles de données, puis les partager avec la communauté sur notre [hub de modèles](https://huggingface.co/models). En même temps, chaque module Python définissant une architecture est complètement indépendant et peut être modifié pour permettre des expériences de recherche rapides.
+
+🤗 Transformers est soutenu par les trois bibliothèques d'apprentissage profond les plus populaires — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) et [TensorFlow](https://www.tensorflow.org/) — avec une intégration transparente entre eux. Il est facile de former vos modèles avec l'un avant de les charger pour l'inférence avec l'autre.
+
+## Démos en ligne
+
+Vous pouvez tester la plupart de nos modèles directement sur leurs pages du [hub de modèles](https://huggingface.co/models). Nous proposons également [l'hébergement privé de modèles, le versionning et une API d'inférence](https://huggingface.co/pricing) pour des modèles publics et privés.
+
+Voici quelques exemples :
+
+En traitement du langage naturel :
+- [Complétion de mots masqués avec BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Reconnaissance d'entités nommées avec Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [Génération de texte avec GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
+- [Inférence de langage naturel avec RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [Résumé avec BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [Réponse aux questions avec DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [Traduction avec T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+En vision par ordinateur :
+- [Classification d'images avec ViT](https://huggingface.co/google/vit-base-patch16-224)
+- [Détection d'objets avec DETR](https://huggingface.co/facebook/detr-resnet-50)
+- [Segmentation sémantique avec SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [Segmentation panoptique avec MaskFormer](https://huggingface.co/facebook/maskformer-swin-small-coco)
+- [Estimation de profondeur avec DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
+- [Classification vidéo avec VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
+- [Segmentation universelle avec OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+En audio :
+- [Reconnaissance automatique de la parole avec Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)
+- [Spotting de mots-clés avec Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [Classification audio avec Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+
+Dans les tâches multimodales :
+- [Réponses aux questions sur table avec TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
+- [Réponses aux questions visuelles avec ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [Classification d'images sans étiquette avec CLIP](https://huggingface.co/openai/clip-vit-large-patch14)
+- [Réponses aux questions sur les documents avec LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
+- [Classification vidéo sans étiquette avec X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
+
+
+## 100 projets utilisant Transformers
+
+Transformers est plus qu'une boîte à outils pour utiliser des modèles pré-entraînés : c'est une communauté de projets construits autour de lui et du Hub Hugging Face. Nous voulons que Transformers permette aux développeurs, chercheurs, étudiants, professeurs, ingénieurs et à quiconque d'imaginer et de réaliser leurs projets de rêve.
+
+Afin de célébrer les 100 000 étoiles de transformers, nous avons décidé de mettre en avant la communauté et avons créé la page [awesome-transformers](./awesome-transformers.md) qui répertorie 100 projets incroyables construits autour de transformers.
+
+Si vous possédez ou utilisez un projet que vous pensez devoir figurer dans la liste, veuillez ouvrir une pull request pour l'ajouter !
+
+## Si vous recherchez un support personnalisé de la part de l'équipe Hugging Face
+
+
+
++ +## Tour rapide + +Pour utiliser immédiatement un modèle sur une entrée donnée (texte, image, audio,...), nous fournissons l'API `pipeline`. Les pipelines regroupent un modèle pré-entraîné avec la préparation des données qui a été utilisée lors de l'entraînement de ce modèle. Voici comment utiliser rapidement un pipeline pour classer des textes en positif ou négatif : + +```python +>>> from transformers import pipeline + +# Allouer un pipeline pour l'analyse de sentiment +>>> classifieur = pipeline('sentiment-analysis') +>>> classifieur("Nous sommes très heureux d'introduire le pipeline dans le référentiel transformers.") +[{'label': 'POSITIF', 'score': 0.9996980428695679}] +``` + +La deuxième ligne de code télécharge et met en cache le modèle pré-entraîné utilisé par le pipeline, tandis que la troisième l'évalue sur le texte donné. Ici, la réponse est "positive" avec une confiance de 99,97%. + +De nombreuses tâches ont une pipeline pré-entraîné prêt à l'emploi, en NLP, mais aussi en vision par ordinateur et en parole. Par exemple, nous pouvons facilement extraire les objets détectés dans une image : + +```python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Télécharger une image avec de jolis chats +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> donnees_image = requests.get(url, stream=True).raw +>>> image = Image.open(donnees_image) + +# Allouer un pipeline pour la détection d'objets +>>> detecteur_objets = pipeline('object-detection') +>>> detecteur_objets(image) +[{'score': 0.9982201457023621, + 'label': 'télécommande', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'télécommande', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'canapé', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'chat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'chat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +Ici, nous obtenons une liste d'objets détectés dans l'image, avec une boîte entourant l'objet et un score de confiance. Voici l'image originale à gauche, avec les prédictions affichées à droite : + +
+
+
+
+
+Vous pouvez en savoir plus sur les tâches supportées par l'API pipeline dans [ce tutoriel](https://huggingface.co/docs/transformers/task_summary).
+
+En plus de `pipeline`, pour télécharger et utiliser n'importe lequel des modèles pré-entraînés sur votre tâche donnée, il suffit de trois lignes de code. Voici la version PyTorch :
+
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+inputs = tokenizer("Bonjour le monde !", return_tensors="pt")
+outputs = model(**inputs)
+```
+
+Et voici le code équivalent pour TensorFlow :
+
+```python
+from transformers import AutoTokenizer, TFAutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+inputs = tokenizer("Bonjour le monde !", return_tensors="tf")
+outputs = model(**inputs)
+```
+
+Le tokenizer est responsable de toutes les étapes de prétraitement que le modèle préentraîné attend et peut être appelé directement sur une seule chaîne de caractères (comme dans les exemples ci-dessus) ou sur une liste. Il produira un dictionnaire que vous pouvez utiliser dans votre code ou simplement passer directement à votre modèle en utilisant l'opérateur de déballage **.
+
+Le modèle lui-même est un module [`nn.Module` PyTorch](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) ou un modèle [`tf.keras.Model` TensorFlow](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (selon votre backend) que vous pouvez utiliser comme d'habitude. [Ce tutoriel](https://huggingface.co/docs/transformers/training) explique comment intégrer un tel modèle dans une boucle d'entraînement classique PyTorch ou TensorFlow, ou comment utiliser notre API `Trainer` pour affiner rapidement sur un nouvel ensemble de données.
+
+## Pourquoi devrais-je utiliser transformers ?
+
+1. Des modèles de pointe faciles à utiliser :
+ - Hautes performances en compréhension et génération de langage naturel, en vision par ordinateur et en tâches audio.
+ - Faible barrière à l'entrée pour les éducateurs et les praticiens.
+ - Peu d'abstractions visibles pour l'utilisateur avec seulement trois classes à apprendre.
+ - Une API unifiée pour utiliser tous nos modèles préentraînés.
+
+1. Coûts informatiques réduits, empreinte carbone plus petite :
+ - Les chercheurs peuvent partager des modèles entraînés au lieu de toujours les réentraîner.
+ - Les praticiens peuvent réduire le temps de calcul et les coûts de production.
+ - Des dizaines d'architectures avec plus de 400 000 modèles préentraînés dans toutes les modalités.
+
+1. Choisissez le bon framework pour chaque partie de la vie d'un modèle :
+ - Entraînez des modèles de pointe en 3 lignes de code.
+ - Transférer un seul modèle entre les frameworks TF2.0/PyTorch/JAX à volonté.
+ - Choisissez facilement le bon framework pour l'entraînement, l'évaluation et la production.
+
+1. Personnalisez facilement un modèle ou un exemple selon vos besoins :
+ - Nous fournissons des exemples pour chaque architecture afin de reproduire les résultats publiés par ses auteurs originaux.
+ - Les détails internes du modèle sont exposés de manière aussi cohérente que possible.
+ - Les fichiers de modèle peuvent être utilisés indépendamment de la bibliothèque pour des expériences rapides.
+
+## Pourquoi ne devrais-je pas utiliser transformers ?
+
+- Cette bibliothèque n'est pas une boîte à outils modulaire de blocs de construction pour les réseaux neuronaux. Le code dans les fichiers de modèle n'est pas refactored avec des abstractions supplémentaires à dessein, afin que les chercheurs puissent itérer rapidement sur chacun des modèles sans plonger dans des abstractions/fichiers supplémentaires.
+- L'API d'entraînement n'est pas destinée à fonctionner avec n'importe quel modèle, mais elle est optimisée pour fonctionner avec les modèles fournis par la bibliothèque. Pour des boucles génériques d'apprentissage automatique, vous devriez utiliser une autre bibliothèque (éventuellement, [Accelerate](https://huggingface.co/docs/accelerate)).
+- Bien que nous nous efforcions de présenter autant de cas d'utilisation que possible, les scripts de notre [dossier d'exemples](https://github.com/huggingface/transformers/tree/main/examples) ne sont que cela : des exemples. Il est prévu qu'ils ne fonctionnent pas immédiatement sur votre problème spécifique et que vous devrez probablement modifier quelques lignes de code pour les adapter à vos besoins.
+
+## Installation
+
+### Avec pip
+
+Ce référentiel est testé sur Python 3.9+, Flax 0.4.1+, PyTorch 2.1+ et TensorFlow 2.6+.
+
+Vous devriez installer 🤗 Transformers dans un [environnement virtuel](https://docs.python.org/3/library/venv.html). Si vous n'êtes pas familier avec les environnements virtuels Python, consultez le [guide utilisateur](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+D'abord, créez un environnement virtuel avec la version de Python que vous allez utiliser et activez-le.
+
+Ensuite, vous devrez installer au moins l'un de Flax, PyTorch ou TensorFlow.
+Veuillez vous référer à la page d'installation de [TensorFlow](https://www.tensorflow.org/install/), de [PyTorch](https://pytorch.org/get-started/locally/#start-locally) et/ou de [Flax](https://github.com/google/flax#quick-install) et [Jax](https://github.com/google/jax#installation) pour connaître la commande d'installation spécifique à votre plateforme.
+
+Lorsqu'un de ces backends est installé, 🤗 Transformers peut être installé avec pip comme suit :
+
+```bash
+pip install transformers
+```
+
+Si vous souhaitez jouer avec les exemples ou avez besoin de la dernière version du code et ne pouvez pas attendre une nouvelle version, vous devez [installer la bibliothèque à partir de la source](https://huggingface.co/docs/transformers/installation#installing-from-source).
+
+### Avec conda
+
+🤗 Transformers peut être installé avec conda comme suit :
+
+```shell
+conda install conda-forge::transformers
+```
+
+> **_NOTE:_** L'installation de `transformers` depuis le canal `huggingface` est obsolète.
+
+Suivez les pages d'installation de Flax, PyTorch ou TensorFlow pour voir comment les installer avec conda.
+
+> **_NOTE:_** Sur Windows, on peut vous demander d'activer le mode développeur pour bénéficier de la mise en cache. Si ce n'est pas une option pour vous, veuillez nous le faire savoir dans [cette issue](https://github.com/huggingface/huggingface_hub/issues/1062).
+
+## Architectures de modèles
+
+**[Tous les points de contrôle](https://huggingface.co/models)** de modèle fournis par 🤗 Transformers sont intégrés de manière transparente depuis le [hub de modèles](https://huggingface.co/models) huggingface.co, où ils sont téléchargés directement par les [utilisateurs](https://huggingface.co/users) et les [organisations](https://huggingface.co/organizations).
+
+Nombre actuel de points de contrôle : 
+
+🤗 Transformers fournit actuellement les architectures suivantes: consultez [ici](https://huggingface.co/docs/transformers/model_summary) pour un résumé global de chacune d'entre elles.
+
+Pour vérifier si chaque modèle a une implémentation en Flax, PyTorch ou TensorFlow, ou s'il a un tokenizer associé pris en charge par la bibliothèque 🤗 Tokenizers, consultez [ce tableau](https://huggingface.co/docs/transformers/index#supported-frameworks).
+
+Ces implémentations ont été testées sur plusieurs ensembles de données (voir les scripts d'exemple) et devraient correspondre aux performances des implémentations originales. Vous pouvez trouver plus de détails sur les performances dans la section Exemples de la [documentation](https://github.com/huggingface/transformers/tree/main/examples).
+
+## En savoir plus
+
+| Section | Description |
+|-|-|
+| [Documentation](https://huggingface.co/docs/transformers/) | Documentation complète de l'API et tutoriels |
+| [Résumé des tâches](https://huggingface.co/docs/transformers/task_summary) | Tâches prises en charge par les 🤗 Transformers |
+| [Tutoriel de prétraitement](https://huggingface.co/docs/transformers/preprocessing) | Utilisation de la classe `Tokenizer` pour préparer les données pour les modèles |
+| [Entraînement et ajustement fin](https://huggingface.co/docs/transformers/training) | Utilisation des modèles fournis par les 🤗 Transformers dans une boucle d'entraînement PyTorch/TensorFlow et de l'API `Trainer` |
+| [Tour rapide : Scripts d'ajustement fin/d'utilisation](https://github.com/huggingface/transformers/tree/main/examples) | Scripts d'exemple pour ajuster finement les modèles sur une large gamme de tâches |
+| [Partage et téléversement de modèles](https://huggingface.co/docs/transformers/model_sharing) | Téléchargez et partagez vos modèles ajustés avec la communauté |
+
+## Citation
+
+Nous disposons désormais d'un [article](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) que vous pouvez citer pour la bibliothèque 🤗 Transformers :
+```bibtex
+@inproceedings{wolf-etal-2020-transformers,
+ title = "Transformers: State-of-the-Art Natural Language Processing",
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+ month = oct,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_hd.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_hd.md
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@@ -0,0 +1,275 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
Jax, PyTorch और TensorFlow के लिए उन्नत मशीन लर्निंग
+ + +
+
+
+
+🤗 Transformers 100 से अधिक भाषाओं में पाठ वर्गीकरण, सूचना निष्कर्षण, प्रश्न उत्तर, सारांशीकरण, अनुवाद, पाठ निर्माण का समर्थन करने के लिए हजारों पूर्व-प्रशिक्षित मॉडल प्रदान करता है। इसका उद्देश्य सबसे उन्नत एनएलपी तकनीक को सभी के लिए सुलभ बनाना है।
+
+🤗 Transformers त्वरित डाउनलोड और उपयोग के लिए एक एपीआई प्रदान करता है, जिससे आप किसी दिए गए पाठ पर एक पूर्व-प्रशिक्षित मॉडल ले सकते हैं, इसे अपने डेटासेट पर ठीक कर सकते हैं और इसे [मॉडल हब](https://huggingface.co/models) के माध्यम से समुदाय के साथ साझा कर सकते हैं। इसी समय, प्रत्येक परिभाषित पायथन मॉड्यूल पूरी तरह से स्वतंत्र है, जो संशोधन और तेजी से अनुसंधान प्रयोगों के लिए सुविधाजनक है।
+
+🤗 Transformers तीन सबसे लोकप्रिय गहन शिक्षण पुस्तकालयों का समर्थन करता है： [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — और इसके साथ निर्बाध रूप से एकीकृत होता है। आप अपने मॉडल को सीधे एक ढांचे के साथ प्रशिक्षित कर सकते हैं और दूसरे के साथ लोड और अनुमान लगा सकते हैं।
+
+## ऑनलाइन डेमो
+
+आप सबसे सीधे मॉडल पृष्ठ पर परीक्षण कर सकते हैं [model hub](https://huggingface.co/models) मॉडल पर। हम [निजी मॉडल होस्टिंग, मॉडल संस्करण, और अनुमान एपीआई](https://huggingface.co/pricing) भी प्रदान करते हैं।。
+
+यहाँ कुछ उदाहरण हैं：
+- [शब्द को भरने के लिए मास्क के रूप में BERT का प्रयोग करें](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [इलेक्ट्रा के साथ नामित इकाई पहचान](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [जीपीटी-2 के साथ टेक्स्ट जनरेशन](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
+- [रॉबर्टा के साथ प्राकृतिक भाषा निष्कर्ष](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [बार्ट के साथ पाठ सारांश](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [डिस्टिलबर्ट के साथ प्रश्नोत्तर](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [अनुवाद के लिए T5 का प्रयोग करें](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+**[Write With Transformer](https://transformer.huggingface.co)**，हगिंग फेस टीम द्वारा बनाया गया, यह एक आधिकारिक पाठ पीढ़ी है demo。
+
+## यदि आप हगिंग फेस टीम से बीस्पोक समर्थन की तलाश कर रहे हैं
+
+
+
++ +## जल्दी शुरू करें + +हम त्वरित उपयोग के लिए मॉडल प्रदान करते हैं `pipeline` (पाइपलाइन) एपीआई। पाइपलाइन पूर्व-प्रशिक्षित मॉडल और संबंधित पाठ प्रीप्रोसेसिंग को एकत्रित करती है। सकारात्मक और नकारात्मक भावना को निर्धारित करने के लिए पाइपलाइनों का उपयोग करने का एक त्वरित उदाहरण यहां दिया गया है: + +```python +>>> from transformers import pipeline + +# भावना विश्लेषण पाइपलाइन का उपयोग करना +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +कोड की दूसरी पंक्ति पाइपलाइन द्वारा उपयोग किए गए पूर्व-प्रशिक्षित मॉडल को डाउनलोड और कैश करती है, जबकि कोड की तीसरी पंक्ति दिए गए पाठ पर मूल्यांकन करती है। यहां उत्तर 99 आत्मविश्वास के स्तर के साथ "सकारात्मक" है। + +कई एनएलपी कार्यों में आउट ऑफ़ द बॉक्स पाइपलाइनों का पूर्व-प्रशिक्षण होता है। उदाहरण के लिए, हम किसी दिए गए पाठ से किसी प्रश्न का उत्तर आसानी से निकाल सकते हैं: + +``` python +>>> from transformers import pipeline + +# प्रश्नोत्तर पाइपलाइन का उपयोग करना +>>> question_answerer = pipeline('question-answering') +>>> question_answerer({ +... 'question': 'What is the name of the repository ?', +... 'context': 'Pipeline has been included in the huggingface/transformers repository' +... }) +{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'} + +``` + +उत्तर देने के अलावा, पूर्व-प्रशिक्षित मॉडल संगत आत्मविश्वास स्कोर भी देता है, जहां उत्तर टोकनयुक्त पाठ में शुरू और समाप्त होता है। आप [इस ट्यूटोरियल](https://huggingface.co/docs/transformers/task_summary) से पाइपलाइन एपीआई द्वारा समर्थित कार्यों के बारे में अधिक जान सकते हैं। + +अपने कार्य पर किसी भी पूर्व-प्रशिक्षित मॉडल को डाउनलोड करना और उसका उपयोग करना भी कोड की तीन पंक्तियों की तरह सरल है। यहाँ PyTorch संस्करण के लिए एक उदाहरण दिया गया है: +```python +>>> from transformers import AutoTokenizer, AutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="pt") +>>> outputs = model(**inputs) +``` +यहाँ समकक्ष है TensorFlow कोड: +```python +>>> from transformers import AutoTokenizer, TFAutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="tf") +>>> outputs = model(**inputs) +``` + +टोकननाइज़र सभी पूर्व-प्रशिक्षित मॉडलों के लिए प्रीप्रोसेसिंग प्रदान करता है और इसे सीधे एक स्ट्रिंग (जैसे ऊपर दिए गए उदाहरण) या किसी सूची पर बुलाया जा सकता है। यह एक डिक्शनरी (तानाशाही) को आउटपुट करता है जिसे आप डाउनस्ट्रीम कोड में उपयोग कर सकते हैं या `**` अनपैकिंग एक्सप्रेशन के माध्यम से सीधे मॉडल को पास कर सकते हैं। + +मॉडल स्वयं एक नियमित [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) या [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (आपके बैकएंड के आधार पर), जो हो सकता है सामान्य तरीके से उपयोग किया जाता है। [यह ट्यूटोरियल](https://huggingface.co/transformers/training.html) बताता है कि इस तरह के मॉडल को क्लासिक PyTorch या TensorFlow प्रशिक्षण लूप में कैसे एकीकृत किया जाए, या हमारे `ट्रेनर` एपीआई का उपयोग कैसे करें ताकि इसे जल्दी से फ़ाइन ट्यून किया जा सके।एक नया डेटासेट पे। + +## ट्रांसफार्मर का उपयोग क्यों करें? + +1. उपयोग में आसानी के लिए उन्नत मॉडल: + - एनएलयू और एनएलजी पर बेहतर प्रदर्शन + - प्रवेश के लिए कम बाधाओं के साथ शिक्षण और अभ्यास के अनुकूल + - उपयोगकर्ता-सामना करने वाले सार तत्व, केवल तीन वर्गों को जानने की जरूरत है + - सभी मॉडलों के लिए एकीकृत एपीआई + +1. कम कम्प्यूटेशनल ओवरहेड और कम कार्बन उत्सर्जन: + - शोधकर्ता हर बार नए सिरे से प्रशिक्षण देने के बजाय प्रशिक्षित मॉडल साझा कर सकते हैं + - इंजीनियर गणना समय और उत्पादन ओवरहेड को कम कर सकते हैं + - दर्जनों मॉडल आर्किटेक्चर, 2,000 से अधिक पूर्व-प्रशिक्षित मॉडल, 100 से अधिक भाषाओं का समर्थन + +1.मॉडल जीवनचक्र के हर हिस्से को शामिल करता है: + - कोड की केवल 3 पंक्तियों में उन्नत मॉडलों को प्रशिक्षित करें + - मॉडल को मनमाने ढंग से विभिन्न डीप लर्निंग फ्रेमवर्क के बीच स्थानांतरित किया जा सकता है, जैसा आप चाहते हैं + - निर्बाध रूप से प्रशिक्षण, मूल्यांकन और उत्पादन के लिए सबसे उपयुक्त ढांचा चुनें + +1. आसानी से अनन्य मॉडल को अनुकूलित करें और अपनी आवश्यकताओं के लिए मामलों का उपयोग करें: + - हम मूल पेपर परिणामों को पुन: पेश करने के लिए प्रत्येक मॉडल आर्किटेक्चर के लिए कई उपयोग के मामले प्रदान करते हैं + - मॉडल की आंतरिक संरचना पारदर्शी और सुसंगत रहती है + - मॉडल फ़ाइल को अलग से इस्तेमाल किया जा सकता है, जो संशोधन और त्वरित प्रयोग के लिए सुविधाजनक है + +## मुझे ट्रांसफॉर्मर का उपयोग कब नहीं करना चाहिए? + +- यह लाइब्रेरी मॉड्यूलर न्यूरल नेटवर्क टूलबॉक्स नहीं है। मॉडल फ़ाइल में कोड जानबूझकर अल्पविकसित है, बिना अतिरिक्त सार इनकैप्सुलेशन के, ताकि शोधकर्ता अमूर्तता और फ़ाइल जंपिंग में शामिल हुए जल्दी से पुनरावृति कर सकें। +- `ट्रेनर` एपीआई किसी भी मॉडल के साथ संगत नहीं है, यह केवल इस पुस्तकालय के मॉडल के लिए अनुकूलित है। यदि आप सामान्य मशीन लर्निंग के लिए उपयुक्त प्रशिक्षण लूप कार्यान्वयन की तलाश में हैं, तो कहीं और देखें। +- हमारे सर्वोत्तम प्रयासों के बावजूद, [उदाहरण निर्देशिका](https://github.com/huggingface/transformers/tree/main/examples) में स्क्रिप्ट केवल उपयोग के मामले हैं। आपकी विशिष्ट समस्या के लिए, वे जरूरी नहीं कि बॉक्स से बाहर काम करें, और आपको कोड की कुछ पंक्तियों को सूट करने की आवश्यकता हो सकती है। + +## स्थापित करना + +### पिप का उपयोग करना + +इस रिपॉजिटरी का परीक्षण Python 3.9+, Flax 0.4.1+, PyTorch 2.1+ और TensorFlow 2.6+ के तहत किया गया है। + +आप [वर्चुअल एनवायरनमेंट](https://docs.python.org/3/library/venv.html) में 🤗 ट्रांसफॉर्मर इंस्टॉल कर सकते हैं। यदि आप अभी तक पायथन के वर्चुअल एनवायरनमेंट से परिचित नहीं हैं, तो कृपया इसे [उपयोगकर्ता निर्देश](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) पढ़ें। + +सबसे पहले, पायथन के उस संस्करण के साथ एक आभासी वातावरण बनाएं जिसका आप उपयोग करने और उसे सक्रिय करने की योजना बना रहे हैं। + +फिर, आपको Flax, PyTorch या TensorFlow में से किसी एक को स्थापित करने की आवश्यकता है। अपने प्लेटफ़ॉर्म पर इन फ़्रेमवर्क को स्थापित करने के लिए, [TensorFlow स्थापना पृष्ठ](https://www.tensorflow.org/install/), [PyTorch स्थापना पृष्ठ](https://pytorch.org/get-started/locally) + +देखें start-locally या [Flax स्थापना पृष्ठ](https://github.com/google/flax#quick-install). + +जब इनमें से कोई एक बैकएंड सफलतापूर्वक स्थापित हो जाता है, तो ट्रांसफॉर्मर निम्नानुसार स्थापित किए जा सकते हैं: + +```bash +pip install transformers +``` + +यदि आप उपयोग के मामलों को आज़माना चाहते हैं या आधिकारिक रिलीज़ से पहले नवीनतम इन-डेवलपमेंट कोड का उपयोग करना चाहते हैं, तो आपको [सोर्स से इंस्टॉल करना होगा](https://huggingface.co/docs/transformers/installation#installing-from-) स्रोत। + +### कोंडा का उपयोग करना + +ट्रांसफॉर्मर कोंडा के माध्यम से निम्नानुसार स्थापित किया जा सकता है: + +```shell script +conda install conda-forge::transformers +``` + +> **_नोट:_** `huggingface` चैनल से `transformers` इंस्टॉल करना पुराना पड़ चुका है। + +कोंडा के माध्यम से Flax, PyTorch, या TensorFlow में से किसी एक को स्थापित करने के लिए, निर्देशों के लिए उनके संबंधित स्थापना पृष्ठ देखें। + +## मॉडल आर्किटेक्चर +[उपयोगकर्ता](https://huggingface.co/users) और [organization](https://huggingface.co) द्वारा ट्रांसफॉर्मर समर्थित [**सभी मॉडल चौकियों**](https://huggingface.co/models/users) हगिंगफेस.को/ऑर्गनाइजेशन), सभी को बिना किसी बाधा के हगिंगफेस.को [मॉडल हब](https://huggingface.co) के साथ एकीकृत किया गया है। + +चौकियों की वर्तमान संख्या:  + +🤗 ट्रांसफॉर्मर वर्तमान में निम्नलिखित आर्किटेक्चर का समर्थन करते हैं: मॉडल के अवलोकन के लिए [यहां देखें](https://huggingface.co/docs/transformers/model_summary)： + +यह जांचने के लिए कि क्या किसी मॉडल में पहले से ही Flax, PyTorch या TensorFlow का कार्यान्वयन है, या यदि उसके पास Tokenizers लाइब्रेरी में संबंधित टोकन है, तो [यह तालिका](https://huggingface.co/docs/transformers/index#supported) देखें। -फ्रेमवर्क)। + +इन कार्यान्वयनों का परीक्षण कई डेटासेट पर किया गया है (देखें केस स्क्रिप्ट का उपयोग करें) और वैनिला कार्यान्वयन के लिए तुलनात्मक रूप से प्रदर्शन करना चाहिए। आप उपयोग के मामले के दस्तावेज़ [इस अनुभाग](https://huggingface.co/docs/transformers/examples) में व्यवहार का विवरण पढ़ सकते हैं। + + +## अधिक समझें + +|अध्याय | विवरण | +|-|-| +| [दस्तावेज़ीकरण](https://huggingface.co/transformers/) | पूरा एपीआई दस्तावेज़ीकरण और ट्यूटोरियल | +| [कार्य सारांश](https://huggingface.co/docs/transformers/task_summary) | ट्रांसफॉर्मर समर्थित कार्य | +| [प्रीप्रोसेसिंग ट्यूटोरियल](https://huggingface.co/docs/transformers/preprocessing) | मॉडल के लिए डेटा तैयार करने के लिए `टोकनाइज़र` का उपयोग करना | +| [प्रशिक्षण और फाइन-ट्यूनिंग](https://huggingface.co/docs/transformers/training) | PyTorch/TensorFlow के ट्रेनिंग लूप या `ट्रेनर` API में ट्रांसफॉर्मर द्वारा दिए गए मॉडल का उपयोग करें | +| [क्विक स्टार्ट: ट्वीकिंग एंड यूज़ केस स्क्रिप्ट्स](https://github.com/huggingface/transformers/tree/main/examples) | विभिन्न कार्यों के लिए केस स्क्रिप्ट का उपयोग करें | +| [मॉडल साझा करना और अपलोड करना](https://huggingface.co/docs/transformers/model_sharing) | समुदाय के साथ अपने फाइन टूनड मॉडल अपलोड और साझा करें | +| [माइग्रेशन](https://huggingface.co/docs/transformers/migration) | `पाइटोरच-ट्रांसफॉर्मर्स` या `पाइटोरच-प्रीट्रेनड-बर्ट` से ट्रांसफॉर्मर में माइग्रेट करना | + +## उद्धरण + +हमने आधिकारिक तौर पर इस लाइब्रेरी का [पेपर](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) प्रकाशित किया है, अगर आप ट्रान्सफ़ॉर्मर्स लाइब्रेरी का उपयोग करते हैं, तो कृपया उद्धृत करें: +```bibtex +@inproceedings{wolf-etal-2020-transformers, + title = "Transformers: State-of-the-Art Natural Language Processing", + author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = oct, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", + pages = "38--45" +} +``` diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_ja.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_ja.md new file mode 100644 index 0000000000000000000000000000000000000000..5d5db499323979ef6d7cc4f9e8449a7b62d4fa2b --- /dev/null +++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_ja.md @@ -0,0 +1,335 @@ + + + + +
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
JAX、PyTorch、TensorFlowのための最先端機械学習
+ + +
+
+
+
+🤗Transformersは、テキスト、視覚、音声などの異なるモダリティに対してタスクを実行するために、事前に学習させた数千のモデルを提供します。
+
+これらのモデルは次のような場合に適用できます:
+
+* 📝 テキストは、テキストの分類、情報抽出、質問応答、要約、翻訳、テキスト生成などのタスクのために、100以上の言語に対応しています。
+* 🖼️ 画像分類、物体検出、セグメンテーションなどのタスクのための画像。
+* 🗣️ 音声は、音声認識や音声分類などのタスクに使用します。
+
+トランスフォーマーモデルは、テーブル質問応答、光学文字認識、スキャン文書からの情報抽出、ビデオ分類、視覚的質問応答など、**複数のモダリティを組み合わせた**タスクも実行可能です。
+
+🤗Transformersは、与えられたテキストに対してそれらの事前学習されたモデルを素早くダウンロードして使用し、あなた自身のデータセットでそれらを微調整し、私たちの[model hub](https://huggingface.co/models)でコミュニティと共有するためのAPIを提供します。同時に、アーキテクチャを定義する各Pythonモジュールは完全にスタンドアロンであり、迅速な研究実験を可能にするために変更することができます。
+
+🤗Transformersは[Jax](https://jax.readthedocs.io/en/latest/)、[PyTorch](https://pytorch.org/)、[TensorFlow](https://www.tensorflow.org/)という3大ディープラーニングライブラリーに支えられ、それぞれのライブラリをシームレスに統合しています。片方でモデルを学習してから、もう片方で推論用にロードするのは簡単なことです。
+
+## オンラインデモ
+
+[model hub](https://huggingface.co/models)から、ほとんどのモデルのページで直接テストすることができます。また、パブリックモデル、プライベートモデルに対して、[プライベートモデルのホスティング、バージョニング、推論API](https://huggingface.co/pricing)を提供しています。
+
+以下はその一例です:
+
+ 自然言語処理にて:
+- [BERTによるマスクドワード補完](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Electraによる名前実体認識](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [GPT-2によるテキスト生成](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
+- [RoBERTaによる自然言語推論](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [BARTによる要約](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [DistilBERTによる質問応答](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [T5による翻訳](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+コンピュータビジョンにて:
+- [ViTによる画像分類](https://huggingface.co/google/vit-base-patch16-224)
+- [DETRによる物体検出](https://huggingface.co/facebook/detr-resnet-50)
+- [SegFormerによるセマンティックセグメンテーション](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [DETRによるパノプティックセグメンテーション](https://huggingface.co/facebook/detr-resnet-50-panoptic)
+
+オーディオにて:
+- [Wav2Vec2による自動音声認識](https://huggingface.co/facebook/wav2vec2-base-960h)
+- [Wav2Vec2によるキーワード検索](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+
+マルチモーダルなタスクにて:
+- [ViLTによる視覚的質問応答](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+
+Hugging Faceチームによって作られた **[トランスフォーマーを使った書き込み](https://transformer.huggingface.co)** は、このリポジトリのテキスト生成機能の公式デモである。
+
+## Hugging Faceチームによるカスタム・サポートをご希望の場合
+
+
+
++ +## クイックツアー + +与えられた入力（テキスト、画像、音声、...）に対してすぐにモデルを使うために、我々は`pipeline`というAPIを提供しております。pipelineは、学習済みのモデルと、そのモデルの学習時に使用された前処理をグループ化したものです。以下は、肯定的なテキストと否定的なテキストを分類するためにpipelineを使用する方法です: + +```python +>>> from transformers import pipeline + +# Allocate a pipeline for sentiment-analysis +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +2行目のコードでは、pipelineで使用される事前学習済みモデルをダウンロードしてキャッシュし、3行目では与えられたテキストに対してそのモデルを評価します。ここでは、答えは99.97%の信頼度で「ポジティブ」です。 + +自然言語処理だけでなく、コンピュータビジョンや音声処理においても、多くのタスクにはあらかじめ訓練された`pipeline`が用意されている。例えば、画像から検出された物体を簡単に抽出することができる: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Download an image with cute cats +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Allocate a pipeline for object detection +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +ここでは、画像から検出されたオブジェクトのリストが得られ、オブジェクトを囲むボックスと信頼度スコアが表示されます。左側が元画像、右側が予測結果を表示したものです: + +
+
+
+
+
+[このチュートリアル](https://huggingface.co/docs/transformers/task_summary)では、`pipeline`APIでサポートされているタスクについて詳しく説明しています。
+
+`pipeline`に加えて、与えられたタスクに学習済みのモデルをダウンロードして使用するために必要なのは、3行のコードだけです。以下はPyTorchのバージョンです:
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+
+そしてこちらはTensorFlowと同等のコードとなります:
+```python
+>>> from transformers import AutoTokenizer, TFAutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="tf")
+>>> outputs = model(**inputs)
+```
+
+トークナイザは学習済みモデルが期待するすべての前処理を担当し、単一の文字列 (上記の例のように) またはリストに対して直接呼び出すことができます。これは下流のコードで使用できる辞書を出力します。また、単純に ** 引数展開演算子を使用してモデルに直接渡すこともできます。
+
+モデル自体は通常の[Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) または [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (バックエンドによって異なる)で、通常通り使用することが可能です。[このチュートリアル](https://huggingface.co/docs/transformers/training)では、このようなモデルを従来のPyTorchやTensorFlowの学習ループに統合する方法や、私たちの`Trainer`APIを使って新しいデータセットで素早く微調整を行う方法について説明します。
+
+## なぜtransformersを使う必要があるのでしょうか？
+
+1. 使いやすい最新モデル:
+ - 自然言語理解・生成、コンピュータビジョン、オーディオの各タスクで高いパフォーマンスを発揮します。
+ - 教育者、実務者にとっての低い参入障壁。
+ - 学習するクラスは3つだけで、ユーザが直面する抽象化はほとんどありません。
+ - 学習済みモデルを利用するための統一されたAPI。
+
+1. 低い計算コスト、少ないカーボンフットプリント:
+ - 研究者は、常に再トレーニングを行うのではなく、トレーニングされたモデルを共有することができます。
+ - 実務家は、計算時間や生産コストを削減することができます。
+ - すべてのモダリティにおいて、60,000以上の事前学習済みモデルを持つ数多くのアーキテクチャを提供します。
+
+1. モデルのライフタイムのあらゆる部分で適切なフレームワークを選択可能:
+ - 3行のコードで最先端のモデルをトレーニング。
+ - TF2.0/PyTorch/JAXフレームワーク間で1つのモデルを自在に移動させる。
+ - 学習、評価、生産に適したフレームワークをシームレスに選択できます。
+
+1. モデルやサンプルをニーズに合わせて簡単にカスタマイズ可能:
+ - 原著者が発表した結果を再現するために、各アーキテクチャの例を提供しています。
+ - モデル内部は可能な限り一貫して公開されています。
+ - モデルファイルはライブラリとは独立して利用することができ、迅速な実験が可能です。
+
+## なぜtransformersを使ってはいけないのでしょうか？
+
+- このライブラリは、ニューラルネットのためのビルディングブロックのモジュール式ツールボックスではありません。モデルファイルのコードは、研究者が追加の抽象化/ファイルに飛び込むことなく、各モデルを素早く反復できるように、意図的に追加の抽象化でリファクタリングされていません。
+- 学習APIはどのようなモデルでも動作するわけではなく、ライブラリが提供するモデルで動作するように最適化されています。一般的な機械学習のループには、別のライブラリ(おそらく[Accelerate](https://huggingface.co/docs/accelerate))を使用する必要があります。
+- 私たちはできるだけ多くの使用例を紹介するよう努力していますが、[examples フォルダ](https://github.com/huggingface/transformers/tree/main/examples) にあるスクリプトはあくまで例です。あなたの特定の問題に対してすぐに動作するわけではなく、あなたのニーズに合わせるために数行のコードを変更する必要があることが予想されます。
+
+## インストール
+
+### pipにて
+
+このリポジトリは、Python 3.9+, Flax 0.4.1+, PyTorch 2.1+, TensorFlow 2.6+ でテストされています。
+
+🤗Transformersは[仮想環境](https://docs.python.org/3/library/venv.html)にインストールする必要があります。Pythonの仮想環境に慣れていない場合は、[ユーザーガイド](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)を確認してください。
+
+まず、使用するバージョンのPythonで仮想環境を作成し、アクティベートします。
+
+その後、Flax, PyTorch, TensorFlowのうち少なくとも1つをインストールする必要があります。
+[TensorFlowインストールページ](https://www.tensorflow.org/install/)、[PyTorchインストールページ](https://pytorch.org/get-started/locally/#start-locally)、[Flax](https://github.com/google/flax#quick-install)、[Jax](https://github.com/google/jax#installation)インストールページで、お使いのプラットフォーム別のインストールコマンドを参照してください。
+
+これらのバックエンドのいずれかがインストールされている場合、🤗Transformersは以下のようにpipを使用してインストールすることができます:
+
+```bash
+pip install transformers
+```
+
+もしサンプルを試したい、またはコードの最先端が必要で、新しいリリースを待てない場合は、[ライブラリをソースからインストール](https://huggingface.co/docs/transformers/installation#installing-from-source)する必要があります。
+
+### condaにて
+
+🤗Transformersは以下のようにcondaを使って設置することができます:
+
+```shell script
+conda install conda-forge::transformers
+```
+
+> **_注意:_** `huggingface` チャンネルから `transformers` をインストールすることは非推奨です。
+
+Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それぞれのインストールページに従ってください。
+
+> **_注意:_** Windowsでは、キャッシュの恩恵を受けるために、デベロッパーモードを有効にするよう促されることがあります。このような場合は、[このissue](https://github.com/huggingface/huggingface_hub/issues/1062)でお知らせください。
+
+## モデルアーキテクチャ
+
+🤗Transformersが提供する **[全モデルチェックポイント](https://huggingface.co/models)** は、[ユーザー](https://huggingface.co/users)や[組織](https://huggingface.co/organizations)によって直接アップロードされるhuggingface.co [model hub](https://huggingface.co)からシームレスに統合されています。
+
+現在のチェックポイント数: 
+
+🤗Transformersは現在、以下のアーキテクチャを提供しています: それぞれのハイレベルな要約は[こちら](https://huggingface.co/docs/transformers/model_summary)を参照してください.
+
+各モデルがFlax、PyTorch、TensorFlowで実装されているか、🤗Tokenizersライブラリに支えられた関連トークナイザを持っているかは、[この表](https://huggingface.co/docs/transformers/index#supported-frameworks)を参照してください。
+
+これらの実装はいくつかのデータセットでテストされており(サンプルスクリプトを参照)、オリジナルの実装の性能と一致するはずである。性能の詳細は[documentation](https://github.com/huggingface/transformers/tree/main/examples)のExamplesセクションで見ることができます。
+
+
+## さらに詳しく
+
+| セクション | 概要 |
+|-|-|
+| [ドキュメント](https://huggingface.co/docs/transformers/) | 完全なAPIドキュメントとチュートリアル |
+| [タスク概要](https://huggingface.co/docs/transformers/task_summary) | 🤗Transformersがサポートするタスク |
+| [前処理チュートリアル](https://huggingface.co/docs/transformers/preprocessing) | モデル用のデータを準備するために`Tokenizer`クラスを使用 |
+| [トレーニングと微調整](https://huggingface.co/docs/transformers/training) | PyTorch/TensorFlowの学習ループと`Trainer`APIで🤗Transformersが提供するモデルを使用 |
+| [クイックツアー: 微調整/使用方法スクリプト](https://github.com/huggingface/transformers/tree/main/examples) | 様々なタスクでモデルの微調整を行うためのスクリプト例 |
+| [モデルの共有とアップロード](https://huggingface.co/docs/transformers/model_sharing) | 微調整したモデルをアップロードしてコミュニティで共有する |
+| [マイグレーション](https://huggingface.co/docs/transformers/migration) | `pytorch-transformers`または`pytorch-pretrained-bert`から🤗Transformers に移行する |
+
+## 引用
+
+🤗 トランスフォーマーライブラリに引用できる[論文](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)が出来ました:
+```bibtex
+@inproceedings{wolf-etal-2020-transformers,
+ title = "Transformers: State-of-the-Art Natural Language Processing",
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+ month = oct,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_ko.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_ko.md
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@@ -0,0 +1,314 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
Jax, Pytorch, TensorFlow를 위한 최첨단 머신러닝
+ + +
+
+
+
+🤗 Transformers는 텍스트, 비전, 오디오와 같은 다양한 분야에서 여러 과제를 수행하는 수천 개의 사전 학습된 모델을 제공합니다.
+
+제공되는 모델을 통해 다음 과제를 수행할 수 있습니다.
+- 📝 텍스트: 100개 이상의 언어들로, 텍스트 분류, 정보 추출, 질문 답변, 요약, 번역 및 문장 생성
+- 🖼️ 이미지: 이미지 분류(Image Classification), 객체 탐지(Object Detection) 및 분할(Segmentation)
+- 🗣️ 오디오: 음성 인식(Speech Recognition) 및 오디오 분류(Audio Classification)
+
+Transformer의 모델은 표를 통한 질의응답(Table QA), 광학 문자 인식(Optical Character Recognition), 스캔 한 문서에서 정보 추출, 비디오 분류 및 시각적 질의응답과 같은 **여러 분야가 결합된** 과제 또한 수행할 수 있습니다.
+
+🤗 Transformers는 이러한 사전학습 모델을 빠르게 다운로드해 특정 텍스트에 사용하고, 원하는 데이터로 fine-tuning해 커뮤니티나 우리의 [모델 허브](https://huggingface.co/models)에 공유할 수 있도록 API를 제공합니다. 또한, 모델 구조를 정의하는 각 파이썬 모듈은 완전히 독립적이여서 연구 실험을 위해 손쉽게 수정할 수 있습니다.
+
+🤗 Transformers는 가장 유명한 3개의 딥러닝 라이브러리를 지원합니다. 이들은 서로 완벽히 연동됩니다 — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/). 간단하게 이 라이브러리 중 하나로 모델을 학습하고, 또 다른 라이브러리로 추론을 위해 모델을 불러올 수 있습니다.
+
+## 온라인 데모
+
+대부분의 모델을 [모델 허브](https://huggingface.co/models) 페이지에서 바로 테스트해 볼 수 있습니다. 공개 및 비공개 모델을 위한 [비공개 모델 호스팅, 버전 관리, 추론 API](https://huggingface.co/pricing)도 제공합니다.
+
+아래 몇 가지 예시가 있습니다:
+
+자연어 처리:
+- [BERT로 마스킹된 단어 완성하기](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Electra를 이용한 개체명 인식](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [GPT-2로 텍스트 생성하기](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
+- [RoBERTa로 자연어 추론하기](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [BART를 이용한 요약](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [DistilBERT를 이용한 질문 답변](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [T5로 번역하기](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+컴퓨터 비전:
+- [ViT와 함께하는 이미지 분류](https://huggingface.co/google/vit-base-patch16-224)
+- [DETR로 객체 탐지하기](https://huggingface.co/facebook/detr-resnet-50)
+- [SegFormer로 의미적 분할(semantic segmentation)하기](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [Mask2Former로 판옵틱 분할(panoptic segmentation)하기](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic)
+- [Depth Anything으로 깊이 추정(depth estimation)하기](https://huggingface.co/docs/transformers/main/model_doc/depth_anything)
+- [VideoMAE와 함께하는 비디오 분류](https://huggingface.co/docs/transformers/model_doc/videomae)
+- [OneFormer로 유니버설 분할(universal segmentation)하기](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+오디오:
+- [Whisper와 함께하는 자동 음성 인식](https://huggingface.co/openai/whisper-large-v3)
+- [Wav2Vec2로 키워드 검출(keyword spotting)하기](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [Audio Spectrogram Transformer로 오디오 분류하기](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+
+멀티 모달(Multimodal Task):
+- [TAPAS로 표 안에서 질문 답변하기](https://huggingface.co/google/tapas-base-finetuned-wtq)
+- [ViLT와 함께하는 시각적 질의응답](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [LLaVa로 이미지에 설명 넣기](https://huggingface.co/llava-hf/llava-1.5-7b-hf)
+- [SigLIP와 함께하는 제로 샷(zero-shot) 이미지 분류](https://huggingface.co/google/siglip-so400m-patch14-384)
+- [LayoutLM으로 문서 안에서 질문 답변하기](https://huggingface.co/impira/layoutlm-document-qa)
+- [X-CLIP과 함께하는 제로 샷(zero-shot) 비디오 분류](https://huggingface.co/docs/transformers/model_doc/xclip)
+- [OWLv2로 진행하는 제로 샷(zero-shot) 객체 탐지](https://huggingface.co/docs/transformers/en/model_doc/owlv2)
+- [CLIPSeg로 진행하는 제로 샷(zero-shot) 이미지 분할](https://huggingface.co/docs/transformers/model_doc/clipseg)
+- [SAM과 함께하는 자동 마스크 생성](https://huggingface.co/docs/transformers/model_doc/sam)
+
+**[Transformer와 글쓰기](https://transformer.huggingface.co)** 는 이 저장소의 텍스트 생성 능력에 관한 Hugging Face 팀의 공식 데모입니다.
+
+## Transformers를 사용한 100개의 프로젝트
+
+Transformers는 사전 학습된 모델들을 이용하는 도구를 넘어 Transformers와 함께 빌드 된 프로젝트 및 Hugging Face Hub를 위한 하나의 커뮤니티입니다. 우리는 Transformers를 통해 개발자, 연구자, 학생, 교수, 엔지니어 및 모든 이들이 꿈을 품은 프로젝트(Dream Project)를 빌드 할 수 있길 바랍니다.
+
+Transformers에 달린 100,000개의 별을 축하하기 위해, 우리는 커뮤니티를 주목하고자 Transformers를 품고 빌드 된 100개의 어마어마한 프로젝트들을 선별하여 [awesome-transformers](https://github.com/huggingface/transformers/blob/main/awesome-transformers.md) 페이지에 나열하였습니다.
+
+만일 소유한 혹은 사용하고 계신 프로젝트가 이 리스트에 꼭 등재되어야 한다고 믿으신다면, PR을 열고 추가하여 주세요!
+
+## 조직 안에서 AI 사용에 대해 진지하게 고민 중이신가요? Hugging Face Enterprise Hub을 통해 더 빨리 구축해 보세요.
+
+
+ + +## 퀵 투어 + +주어진 입력(텍스트, 이미지, 오디오, ...)에 바로 모델을 사용할 수 있도록, 우리는 `pipeline` API를 제공합니다. Pipeline은 사전학습 모델과 그 모델을 학습할 때 적용한 전처리 방식을 하나로 합칩니다. 다음은 긍정적인 텍스트와 부정적인 텍스트를 분류하기 위해 pipeline을 사용한 간단한 예시입니다: + +```python +>>> from transformers import pipeline + +# 감정 분석 파이프라인을 할당하세요 +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +코드의 두 번째 줄은 pipeline이 사용하는 사전학습 모델을 다운로드하고 캐시로 저장합니다. 세 번째 줄에선 그 모델이 주어진 텍스트를 평가합니다. 여기서 모델은 99.97%의 확률로 텍스트가 긍정적이라고 평가했습니다. + +자연어 처리(NLP) 뿐만 아니라 컴퓨터 비전, 발화(Speech) 과제들을 사전 학습된 `pipeline`으로 바로 수행할 수 있습니다. 예를 들어, 사진에서 손쉽게 객체들을 탐지할 수 있습니다.: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# 귀여운 고양이가 있는 이미지를 다운로드하세요 +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# 객체 감지를 위한 파이프라인을 할당하세요 +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` +위와 같이, 우리는 이미지에서 탐지된 객체들에 대하여 객체를 감싸는 박스와 확률 리스트를 얻을 수 있습니다. 왼쪽이 원본 이미지이며 오른쪽은 해당 이미지에 탐지된 결과를 표시하였습니다. +
+
+
+
+
+[이 튜토리얼](https://huggingface.co/docs/transformers/ko/task_summary)에서 `pipeline` API가 지원하는 다양한 과제를 확인할 수 있습니다.
+
+코드 3줄로 원하는 과제에 맞게 사전학습 모델을 다운로드 받고 사용할 수 있습니다. 다음은 PyTorch 버전입니다:
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+다음은 TensorFlow 버전입니다:
+```python
+>>> from transformers import AutoTokenizer, TFAutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="tf")
+>>> outputs = model(**inputs)
+```
+
+토크나이저는 사전학습 모델의 모든 전처리를 책임집니다. 그리고 (위의 예시처럼) 1개의 스트링이나 리스트도 처리할 수 있습니다. 토크나이저는 딕셔너리를 반환하는데, 이는 다운스트림 코드에 사용하거나 언패킹 연산자 ** 를 이용해 모델에 바로 전달할 수도 있습니다.
+
+모델 자체는 일반적으로 사용되는 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)이나 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)입니다. [이 튜토리얼](https://huggingface.co/docs/transformers/ko/training)은 이러한 모델을 표준적인 PyTorch나 TensorFlow 학습 과정에서 사용하는 방법, 또는 새로운 데이터로 파인 튜닝하기 위해 `Trainer` API를 사용하는 방법을 설명해 줍니다.
+
+## 왜 transformers를 사용해야 할까요?
+
+1. 손쉽게 사용할 수 있는 최첨단 모델:
+ - 자연어 이해(NLU)와 생성(NLG), 컴퓨터 비전, 오디오 과제에서 뛰어난 성능을 보입니다.
+ - 교육자와 실무자에게 진입 장벽이 낮습니다.
+ - 3개의 클래스만 배우면 바로 사용할 수 있습니다.
+ - 하나의 API로 모든 사전학습 모델을 사용할 수 있습니다.
+
+1. 더 적은 계산 비용, 더 적은 탄소 발자국:
+ - 연구자들은 모델을 계속 다시 학습시키는 대신 학습된 모델을 공유할 수 있습니다.
+ - 실무자들은 학습에 필요한 시간과 비용을 절약할 수 있습니다.
+ - 모든 분야를 통틀어서 400,000개 이상의 사전 학습된 모델이 있는 수십 개의 아키텍처.
+
+1. 모델의 각 생애주기에 적합한 프레임워크:
+ - 코드 3줄로 최첨단 모델을 학습하세요.
+ - 목적에 알맞게 모델을 TF2.0/Pytorch/Jax 프레임 워크 중 하나로 이동시키세요.
+ - 학습, 평가, 공개 등 각 단계에 맞는 프레임워크를 원하는대로 선택하세요.
+
+1. 필요한 대로 모델이나 예시를 커스터마이즈하세요:
+ - 우리는 저자가 공개한 결과를 재현하기 위해 각 모델 구조의 예시를 제공합니다.
+ - 모델 내부 구조는 가능한 일관적으로 공개되어 있습니다.
+ - 빠른 실험을 위해 모델 파일은 라이브러리와 독립적으로 사용될 수 있습니다.
+
+## 왜 transformers를 사용하지 말아야 할까요?
+
+- 이 라이브러리는 신경망 블록을 만들기 위한 모듈이 아닙니다. 연구자들이 여러 파일을 살펴보지 않고 바로 각 모델을 사용할 수 있도록, 모델 파일 코드의 추상화 수준을 적정하게 유지했습니다.
+- 학습 API는 모든 모델에 적용할 수 있도록 만들어지진 않았지만, 라이브러리가 제공하는 모델들에 적용할 수 있도록 최적화되었습니다. 일반적인 머신 러닝을 위해선, 다른 라이브러리를 사용하세요(예를 들면, [Accelerate](https://huggingface.co/docs/accelerate/index)).
+- 가능한 많은 사용 예시를 보여드리고 싶어서, [예시 폴더](https://github.com/huggingface/transformers/tree/main/examples)의 스크립트를 준비했습니다. 이 스크립트들을 수정 없이 특정한 문제에 바로 적용하지 못할 수 있습니다. 필요에 맞게 일부 코드를 수정해야 할 수 있습니다.
+
+## 설치
+
+### pip로 설치하기
+
+이 저장소는 Python 3.9+, Flax 0.4.1+, PyTorch 2.1+, TensorFlow 2.6+에서 테스트 되었습니다.
+
+[가상 환경](https://docs.python.org/3/library/venv.html)에 🤗 Transformers를 설치하세요. Python 가상 환경에 익숙하지 않다면, [사용자 가이드](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)를 확인하세요.
+
+우선, 사용할 Python 버전으로 가상 환경을 만들고 실행하세요.
+
+그 다음, Flax, PyTorch, TensorFlow 중 적어도 하나는 설치해야 합니다.
+플랫폼에 맞는 설치 명령어를 확인하기 위해 [TensorFlow 설치 페이지](https://www.tensorflow.org/install/), [PyTorch 설치 페이지](https://pytorch.org/get-started/locally/#start-locally), [Flax 설치 페이지](https://github.com/google/flax#quick-install)를 확인하세요.
+
+이들 중 적어도 하나가 설치되었다면, 🤗 Transformers는 다음과 같이 pip을 이용해 설치할 수 있습니다:
+
+```bash
+pip install transformers
+```
+
+예시들을 체험해보고 싶거나, 최최최첨단 코드를 원하거나, 새로운 버전이 나올 때까지 기다릴 수 없다면 [라이브러리를 소스에서 바로 설치](https://huggingface.co/docs/transformers/ko/installation#install-from-source)하셔야 합니다.
+
+### conda로 설치하기
+
+🤗 Transformers는 다음과 같이 conda로 설치할 수 있습니다:
+
+```shell script
+conda install conda-forge::transformers
+```
+
+> **_노트:_** `huggingface` 채널에서 `transformers`를 설치하는 것은 사용이 중단되었습니다.
+
+Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는 방법을 확인하세요.
+
+> **_노트:_** 윈도우 환경에서 캐싱의 이점을 위해 개발자 모드를 활성화할 수 있습니다. 만약 여러분에게 있어서 선택이 아닌 필수라면 [이 이슈](https://github.com/huggingface/huggingface_hub/issues/1062)를 통해 알려주세요.
+
+## 모델 구조
+
+**🤗 Transformers가 제공하는 [모든 모델 체크포인트](https://huggingface.co/models)** 는 huggingface.co [모델 허브](https://huggingface.co/models)에 완벽히 연동되어 있습니다. [개인](https://huggingface.co/users)과 [기관](https://huggingface.co/organizations)이 모델 허브에 직접 업로드할 수 있습니다.
+
+현재 사용 가능한 모델 체크포인트의 개수: 
+
+🤗 Transformers는 다음 모델들을 제공합니다: 각 모델의 요약은 [여기](https://huggingface.co/docs/transformers/ko/model_summary)서 확인하세요.
+
+각 모델이 Flax, PyTorch, TensorFlow으로 구현되었는지 또는 🤗 Tokenizers 라이브러리가 지원하는 토크나이저를 사용하는지 확인하려면, [이 표](https://huggingface.co/docs/transformers/ko/index#supported-framework)를 확인하세요.
+
+이 구현은 여러 데이터로 검증되었고 (예시 스크립트를 참고하세요) 오리지널 구현의 성능과 같아야 합니다. [도큐먼트](https://github.com/huggingface/transformers/tree/main/examples)의 Examples 섹션에서 성능에 대한 자세한 설명을 확인할 수 있습니다.
+
+## 더 알아보기
+
+| 섹션 | 설명 |
+|-|-|
+| [도큐먼트](https://huggingface.co/transformers/ko/) | 전체 API 도큐먼트와 튜토리얼 |
+| [과제 요약](https://huggingface.co/docs/transformers/ko/task_summary) | 🤗 Transformers가 지원하는 과제들 |
+| [전처리 튜토리얼](https://huggingface.co/docs/transformers/ko/preprocessing) | `Tokenizer` 클래스를 이용해 모델을 위한 데이터 준비하기 |
+| [학습과 파인 튜닝](https://huggingface.co/docs/transformers/ko/training) | 🤗 Transformers가 제공하는 모델 PyTorch/TensorFlow 학습 과정과 `Trainer` API에서 사용하기 |
+| [퀵 투어: 파인 튜닝/사용 스크립트](https://github.com/huggingface/transformers/tree/main/examples) | 다양한 과제에서 모델을 파인 튜닝하는 예시 스크립트 |
+| [모델 공유 및 업로드](https://huggingface.co/docs/transformers/ko/model_sharing) | 커뮤니티에 파인 튜닝된 모델을 업로드 및 공유하기 |
+
+## 인용
+
+🤗 Transformers 라이브러리를 인용하고 싶다면, 이 [논문](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)을 인용해 주세요:
+```bibtex
+@inproceedings{wolf-etal-2020-transformers,
+ title = "Transformers: State-of-the-Art Natural Language Processing",
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+ month = oct,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_pt-br.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_pt-br.md
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@@ -0,0 +1,327 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
Aprendizado de máquina de última geração para JAX, PyTorch e TensorFlow
+ + +
+
+
+
+
+A biblioteca 🤗 Transformers oferece milhares de modelos pré-treinados para executar tarefas em diferentes modalidades, como texto, visão e áudio.
+
+Esses modelos podem ser aplicados a:
+
+* 📝 Texto, para tarefas como classificação de texto, extração de informações, resposta a perguntas, sumarização, tradução, geração de texto, em mais de 100 idiomas.
+* 🖼️ Imagens, para tarefas como classificação de imagens, detecção de objetos e segmentação.
+* 🗣️ Áudio, para tarefas como reconhecimento de fala e classificação de áudio.
+
+Os modelos Transformer também podem executar tarefas em diversas modalidades combinadas, como responder a perguntas em tabelas, reconhecimento óptico de caracteres, extração de informações de documentos digitalizados, classificação de vídeo e resposta a perguntas visuais.
+
+
+A biblioteca 🤗 Transformers oferece APIs para baixar e usar rapidamente esses modelos pré-treinados em um texto específico, ajustá-los em seus próprios conjuntos de dados e, em seguida, compartilhá-los com a comunidade em nosso [model hub](https://huggingface.co/models). Ao mesmo tempo, cada módulo Python que define uma arquitetura é totalmente independente e pode ser modificado para permitir experimentos de pesquisa rápidos.
+
+A biblioteca 🤗 Transformers é respaldada pelas três bibliotecas de aprendizado profundo mais populares — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) e [TensorFlow](https://www.tensorflow.org/) — com uma integração perfeita entre elas. É simples treinar seus modelos com uma delas antes de carregá-los para inferência com a outra
+
+## Demonstração Online
+
+Você pode testar a maioria de nossos modelos diretamente em suas páginas a partir do [model hub](https://huggingface.co/models). Também oferecemos [hospedagem de modelos privados, versionamento e uma API de inferência](https://huggingface.co/pricing)
+para modelos públicos e privados.
+
+Aqui estão alguns exemplos:
+
+Em Processamento de Linguagem Natural:
+
+- [Completar palavra mascarada com BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Reconhecimento de Entidades Nomeadas com Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [Geração de texto com GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C)
+- [Inferência de Linguagem Natural com RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [Sumarização com BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [Resposta a perguntas com DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [Tradução com T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+
+Em Visão Computacional:
+- [Classificação de Imagens com ViT](https://huggingface.co/google/vit-base-patch16-224)
+- [Detecção de Objetos com DETR](https://huggingface.co/facebook/detr-resnet-50)
+- [Segmentação Semântica com SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [Segmentação Panóptica com MaskFormer](https://huggingface.co/facebook/maskformer-swin-small-coco)
+- [Estimativa de Profundidade com DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
+- [Classificação de Vídeo com VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
+- [Segmentação Universal com OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+
+Em Áudio:
+- [Reconhecimento Automático de Fala com Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)
+- [Detecção de Palavras-Chave com Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [Classificação de Áudio com Transformer de Espectrograma de Áudio](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+
+Em Tarefas Multimodais:
+- [Respostas de Perguntas em Tabelas com TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
+- [Respostas de Perguntas Visuais com ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [Classificação de Imagens sem Anotação com CLIP](https://huggingface.co/openai/clip-vit-large-patch14)
+- [Respostas de Perguntas em Documentos com LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
+- [Classificação de Vídeo sem Anotação com X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
+
+## 100 Projetos Usando Transformers
+
+Transformers é mais do que um conjunto de ferramentas para usar modelos pré-treinados: é uma comunidade de projetos construídos ao seu redor e o Hugging Face Hub. Queremos que o Transformers permita que desenvolvedores, pesquisadores, estudantes, professores, engenheiros e qualquer outra pessoa construa seus projetos dos sonhos.
+
+Para celebrar as 100.000 estrelas do Transformers, decidimos destacar a comunidade e criamos a página [awesome-transformers](./awesome-transformers.md), que lista 100 projetos incríveis construídos nas proximidades dos Transformers.
+
+Se você possui ou utiliza um projeto que acredita que deveria fazer parte da lista, abra um PR para adicioná-lo!
+
+## Se você está procurando suporte personalizado da equipe Hugging Face
+
+
+
++ + +## Tour Rápido + +Para usar imediatamente um modelo em uma entrada específica (texto, imagem, áudio, ...), oferecemos a API `pipeline`. Os pipelines agrupam um modelo pré-treinado com o pré-processamento que foi usado durante o treinamento desse modelo. Aqui está como usar rapidamente um pipeline para classificar textos como positivos ou negativos: + +```python +from transformers import pipeline + +# Carregue o pipeline de classificação de texto +>>> classifier = pipeline("sentiment-analysis") + +# Classifique o texto como positivo ou negativo +>>> classifier("Estamos muito felizes em apresentar o pipeline no repositório dos transformers.") +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +A segunda linha de código baixa e armazena em cache o modelo pré-treinado usado pelo pipeline, enquanto a terceira linha o avalia no texto fornecido. Neste exemplo, a resposta é "positiva" com uma confiança de 99,97%. + +Muitas tarefas têm um `pipeline` pré-treinado pronto para uso, não apenas em PNL, mas também em visão computacional e processamento de áudio. Por exemplo, podemos facilmente extrair objetos detectados em uma imagem: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Download an image with cute cats +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Allocate a pipeline for object detection +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + + +Aqui obtemos uma lista de objetos detectados na imagem, com uma caixa envolvendo o objeto e uma pontuação de confiança. Aqui está a imagem original à esquerda, com as previsões exibidas à direita: + +
+
+
+
+
+Você pode aprender mais sobre as tarefas suportadas pela API `pipeline` em [este tutorial](https://huggingface.co/docs/transformers/task_summary).
+
+
+Além do `pipeline`, para baixar e usar qualquer um dos modelos pré-treinados em sua tarefa específica, tudo o que é necessário são três linhas de código. Aqui está a versão em PyTorch:
+
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+
+E aqui está o código equivalente para TensorFlow:
+
+```python
+>>> from transformers import AutoTokenizer, TFAutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="tf")
+>>> outputs = model(**inputs)
+```
+
+O tokenizador é responsável por todo o pré-processamento que o modelo pré-treinado espera, e pode ser chamado diretamente em uma única string (como nos exemplos acima) ou em uma lista. Ele produzirá um dicionário que você pode usar no código subsequente ou simplesmente passar diretamente para o seu modelo usando o operador de descompactação de argumentos **.
+
+O modelo em si é um [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) ou um [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)(dependendo do seu back-end) que você pode usar como de costume. [Este tutorial](https://huggingface.co/docs/transformers/training) explica como integrar esse modelo em um ciclo de treinamento clássico do PyTorch ou TensorFlow, ou como usar nossa API `Trainer` para ajuste fino rápido em um novo conjunto de dados.
+
+## Por que devo usar transformers?
+
+1. Modelos state-of-the-art fáceis de usar:
+ - Alto desempenho em compreensão e geração de linguagem natural, visão computacional e tarefas de áudio.
+ - Barreira de entrada baixa para educadores e profissionais.
+ - Poucas abstrações visíveis para o usuário, com apenas três classes para aprender.
+ - Uma API unificada para usar todos os nossos modelos pré-treinados.
+
+1. Menores custos de computação, menor pegada de carbono:
+ - Pesquisadores podem compartilhar modelos treinados em vez de treinar sempre do zero.
+ - Profissionais podem reduzir o tempo de computação e os custos de produção.
+ - Dezenas de arquiteturas com mais de 60.000 modelos pré-treinados em todas as modalidades.
+
+1. Escolha o framework certo para cada parte da vida de um modelo:
+ - Treine modelos state-of-the-art em 3 linhas de código.
+ - Mova um único modelo entre frameworks TF2.0/PyTorch/JAX à vontade.
+ - Escolha o framework certo de forma contínua para treinamento, avaliação e produção.
+
+1. Personalize facilmente um modelo ou um exemplo para atender às suas necessidades:
+ - Fornecemos exemplos para cada arquitetura para reproduzir os resultados publicados pelos autores originais.
+ - Os detalhes internos do modelo são expostos de maneira consistente.
+ - Os arquivos do modelo podem ser usados de forma independente da biblioteca para experimentos rápidos.
+
+## Por que não devo usar transformers?
+
+- Esta biblioteca não é uma caixa de ferramentas modular para construir redes neurais. O código nos arquivos do modelo não é refatorado com abstrações adicionais de propósito, para que os pesquisadores possam iterar rapidamente em cada um dos modelos sem se aprofundar em abstrações/arquivos adicionais.
+- A API de treinamento não é projetada para funcionar com qualquer modelo, mas é otimizada para funcionar com os modelos fornecidos pela biblioteca. Para loops de aprendizado de máquina genéricos, você deve usar outra biblioteca (possivelmente, [Accelerate](https://huggingface.co/docs/accelerate)).
+- Embora nos esforcemos para apresentar o maior número possível de casos de uso, os scripts em nossa [pasta de exemplos](https://github.com/huggingface/transformers/tree/main/examples) são apenas isso: exemplos. É esperado que eles não funcionem prontos para uso em seu problema específico e que seja necessário modificar algumas linhas de código para adaptá-los às suas necessidades.
+
+
+
+### Com pip
+
+Este repositório é testado no Python 3.9+, Flax 0.4.1+, PyTorch 2.1+ e TensorFlow 2.6+.
+
+Você deve instalar o 🤗 Transformers em um [ambiente virtual](https://docs.python.org/3/library/venv.html). Se você não está familiarizado com ambientes virtuais em Python, confira o [guia do usuário](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+Primeiro, crie um ambiente virtual com a versão do Python que você vai usar e ative-o.
+
+Em seguida, você precisará instalar pelo menos um dos back-ends Flax, PyTorch ou TensorFlow.
+Consulte a [página de instalação do TensorFlow](https://www.tensorflow.org/install/), a [página de instalação do PyTorch](https://pytorch.org/get-started/locally/#start-locally) e/ou [Flax](https://github.com/google/flax#quick-install) e [Jax](https://github.com/google/jax#installation) páginas de instalação para obter o comando de instalação específico para a sua plataforma.
+
+Quando um desses back-ends estiver instalado, o 🤗 Transformers pode ser instalado usando pip da seguinte forma:
+
+```bash
+pip install transformers
+```
+Se você deseja experimentar com os exemplos ou precisa da versão mais recente do código e não pode esperar por um novo lançamento, você deve instalar a [biblioteca a partir do código-fonte](https://huggingface.co/docs/transformers/installation#installing-from-source).
+
+### Com conda
+
+O 🤗 Transformers pode ser instalado com conda da seguinte forma:
+
+```bash
+conda install conda-forge::transformers
+```
+
+> **_NOTA:_** Instalar `transformers` pelo canal `huggingface` está obsoleto.
+
+Siga as páginas de instalação do Flax, PyTorch ou TensorFlow para ver como instalá-los com conda.
+
+Siga as páginas de instalação do Flax, PyTorch ou TensorFlow para ver como instalá-los com o conda.
+
+> **_NOTA:_** No Windows, você pode ser solicitado a ativar o Modo de Desenvolvedor para aproveitar o cache. Se isso não for uma opção para você, por favor nos avise [neste problema](https://github.com/huggingface/huggingface_hub/issues/1062).
+
+## Arquiteturas de Modelos
+
+**[Todos os pontos de verificação de modelo](https://huggingface.co/models)** fornecidos pelo 🤗 Transformers são integrados de forma transparente do [model hub](https://huggingface.co/models) do huggingface.co, onde são carregados diretamente por [usuários](https://huggingface.co/users) e [organizações](https://huggingface.co/organizations).
+
+Número atual de pontos de verificação: 
+
+🤗 Transformers atualmente fornece as seguintes arquiteturas: veja [aqui](https://huggingface.co/docs/transformers/model_summary) para um resumo de alto nível de cada uma delas.
+
+Para verificar se cada modelo tem uma implementação em Flax, PyTorch ou TensorFlow, ou possui um tokenizador associado com a biblioteca 🤗 Tokenizers, consulte [esta tabela](https://huggingface.co/docs/transformers/index#supported-frameworks).
+
+Essas implementações foram testadas em vários conjuntos de dados (veja os scripts de exemplo) e devem corresponder ao desempenho das implementações originais. Você pode encontrar mais detalhes sobre o desempenho na seção de Exemplos da [documentação](https://github.com/huggingface/transformers/tree/main/examples).
+
+
+## Saiba mais
+
+| Seção | Descrição |
+|-|-|
+| [Documentação](https://huggingface.co/docs/transformers/) | Documentação completa da API e tutoriais |
+| [Resumo de Tarefas](https://huggingface.co/docs/transformers/task_summary) | Tarefas suportadas pelo 🤗 Transformers |
+| [Tutorial de Pré-processamento](https://huggingface.co/docs/transformers/preprocessing) | Usando a classe `Tokenizer` para preparar dados para os modelos |
+| [Treinamento e Ajuste Fino](https://huggingface.co/docs/transformers/training) | Usando os modelos fornecidos pelo 🤗 Transformers em um loop de treinamento PyTorch/TensorFlow e a API `Trainer` |
+| [Tour Rápido: Scripts de Ajuste Fino/Utilização](https://github.com/huggingface/transformers/tree/main/examples) | Scripts de exemplo para ajuste fino de modelos em uma ampla gama de tarefas |
+| [Compartilhamento e Envio de Modelos](https://huggingface.co/docs/transformers/model_sharing) | Envie e compartilhe seus modelos ajustados com a comunidade |
+
+## Citação
+
+Agora temos um [artigo](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) que você pode citar para a biblioteca 🤗 Transformers:
+```bibtex
+@inproceedings{wolf-etal-2020-transformers,
+ title = "Transformers: State-of-the-Art Natural Language Processing",
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+ month = out,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_ru.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_ru.md
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index 0000000000000000000000000000000000000000..c30237fef88580201ca30139a8f21e4839a76724
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@@ -0,0 +1,317 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + +
+
Современное машинное обучение для JAX, PyTorch и TensorFlow
+ + +
+
+
+
+🤗 Transformers предоставляет тысячи предварительно обученных моделей для выполнения различных задач, таких как текст, зрение и аудио.
+
+Эти модели могут быть применены к:
+
+* 📝 Тексту для таких задач, как классификация текстов, извлечение информации, ответы на вопросы, обобщение, перевод, генерация текстов на более чем 100 языках.
+* 🖼️ Изображениям для задач классификации изображений, обнаружения объектов и сегментации.
+* 🗣️ Аудио для задач распознавания речи и классификации аудио.
+
+Модели transformers также могут выполнять несколько задач, такие как ответы на табличные вопросы, распознавание оптических символов, извлечение информации из отсканированных документов, классификация видео и ответы на визуальные вопросы.
+
+🤗 Transformers предоставляет API для быстрой загрузки и использования предварительно обученных моделей, их тонкой настройки на собственных датасетах и последующего взаимодействия ими с сообществом на нашем [сайте](https://huggingface.co/models). В то же время каждый python модуль, определяющий архитектуру, полностью автономен и может быть модифицирован для проведения быстрых исследовательских экспериментов.
+
+🤗 Transformers опирается на три самые популярные библиотеки глубокого обучения - [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) и [TensorFlow](https://www.tensorflow.org/) - и легко интегрируется между ними. Это позволяет легко обучать модели с помощью одной из них, а затем загружать их для выводов с помощью другой.
+
+## Онлайн демонстрация
+
+Большинство наших моделей можно протестировать непосредственно на их страницах с [сайта](https://huggingface.co/models). Мы также предлагаем [приватный хостинг моделей, контроль версий и API для выводов](https://huggingface.co/pricing) для публичных и частных моделей.
+
+Вот несколько примеров:
+
+В области NLP ( Обработка текстов на естественном языке ):
+- [Маскированное заполнение слов с помощью BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Распознавание сущностей с помощью Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [Генерация текста с помощью GPT-2](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
+- [Выводы на естественном языке с помощью RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [Обобщение с помощью BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [Ответы на вопросы с помощью DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [Перевод с помощью T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+В области компьютерного зрения:
+- [Классификация изображений с помощью ViT](https://huggingface.co/google/vit-base-patch16-224)
+- [Обнаружение объектов с помощью DETR](https://huggingface.co/facebook/detr-resnet-50)
+- [Семантическая сегментация с помощью SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [Сегментация паноптикума с помощью MaskFormer](https://huggingface.co/facebook/maskformer-swin-small-coco)
+- [Оценка глубины с помощью DPT](https://huggingface.co/docs/transformers/model_doc/dpt)
+- [Классификация видео с помощью VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
+- [Универсальная сегментация с помощью OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+В области звука:
+- [Автоматическое распознавание речи с помощью Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)
+- [Поиск ключевых слов с помощью Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [Классификация аудиоданных с помощью траснформера аудиоспектрограмм](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+
+В мультимодальных задачах:
+- [Ответы на вопросы по таблице с помощью TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
+- [Визуальные ответы на вопросы с помощью ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [Zero-shot классификация изображений с помощью CLIP](https://huggingface.co/openai/clip-vit-large-patch14)
+- [Ответы на вопросы по документам с помощью LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
+- [Zero-shot классификация видео с помощью X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
+
+
+## 100 проектов, использующих Transformers
+
+Transformers - это не просто набор инструментов для использования предварительно обученных моделей: это сообщество проектов, созданное на его основе, и
+Hugging Face Hub. Мы хотим, чтобы Transformers позволил разработчикам, исследователям, студентам, профессорам, инженерам и всем желающим
+создавать проекты своей мечты.
+
+Чтобы отпраздновать 100 тысяч звезд Transformers, мы решили сделать акцент на сообществе, и создали страницу [awesome-transformers](./awesome-transformers.md), на которой перечислены 100
+невероятных проектов, созданных с помощью transformers.
+
+Если вы являетесь владельцем или пользователем проекта, который, по вашему мнению, должен быть включен в этот список, пожалуйста, откройте PR для его добавления!
+
+## Если вы хотите получить индивидуальную поддержку от команды Hugging Face
+
+
+
++ +## Быстрый гайд + +Для использования модели на заданном входе (текст, изображение, звук, ...) мы предоставляем API `pipeline`. Конвейеры объединяют предварительно обученную модель с препроцессингом, который использовался при ее обучении. Вот как можно быстро использовать конвейер для классификации положительных и отрицательных текстов: + +```python +>>> from transformers import pipeline + +# Выделение конвейера для анализа настроений +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('Мы очень рады представить конвейер в transformers.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +Вторая строка кода загружает и кэширует предварительно обученную модель, используемую конвейером, а третья оценивает ее на заданном тексте. Здесь ответ "POSITIVE" с уверенностью 99,97%. + +Во многих задачах, как в НЛП, так и в компьютерном зрении и речи, уже есть готовый `pipeline`. Например, мы можем легко извлечь обнаруженные объекты на изображении: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Скачиваем изображение с милыми котиками +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Выделение конвейера для обнаружения объектов +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +Здесь мы получаем список объектов, обнаруженных на изображении, с рамкой вокруг объекта и оценкой достоверности. Слева - исходное изображение, справа прогнозы: + +
+
+
+
+
+Подробнее о задачах, поддерживаемых API `pipeline`, можно узнать в [этом учебном пособии](https://huggingface.co/docs/transformers/task_sum)
+
+В дополнение к `pipeline`, для загрузки и использования любой из предварительно обученных моделей в заданной задаче достаточно трех строк кода. Вот версия для PyTorch:
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Привет мир!", return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+
+А вот эквивалентный код для TensorFlow:
+```python
+>>> from transformers import AutoTokenizer, TFAutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Привет мир!", return_tensors="tf")
+>>> outputs = model(**inputs)
+```
+
+Токенизатор отвечает за всю предварительную обработку, которую ожидает предварительно обученная модель, и может быть вызван непосредственно с помощью одной строки (как в приведенных выше примерах) или на списке. В результате будет получен словарь, который можно использовать в последующем коде или просто напрямую передать в модель с помощью оператора распаковки аргументов **.
+
+Сама модель представляет собой обычный [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) или [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (в зависимости от используемого бэкенда), который можно использовать как обычно. [В этом руководстве](https://huggingface.co/docs/transformers/training) рассказывается, как интегрировать такую модель в классический цикл обучения PyTorch или TensorFlow, или как использовать наш API `Trainer` для быстрой тонкой настройки на новом датасете.
+
+## Почему необходимо использовать transformers?
+
+1. Простые в использовании современные модели:
+ - Высокая производительность в задачах понимания и генерации естественного языка, компьютерного зрения и аудио.
+ - Низкий входной барьер для преподавателей и практиков.
+ - Небольшое количество абстракций для пользователя и всего три класса для изучения.
+ - Единый API для использования всех наших предварительно обученных моделей.
+
+1. Более низкие вычислительные затраты, меньший "углеродный след":
+ - Исследователи могут обмениваться обученными моделями вместо того, чтобы постоянно их переобучать.
+ - Практики могут сократить время вычислений и производственные затраты.
+ - Десятки архитектур с более чем 60 000 предварительно обученных моделей для всех модальностей.
+
+1. Выбор подходящего фреймворка для каждого этапа жизни модели:
+ - Обучение самых современных моделей за 3 строки кода.
+ - Перемещайте одну модель между фреймворками TF2.0/PyTorch/JAX по своему усмотрению.
+ - Беспрепятственный выбор подходящего фреймворка для обучения, оценки и производства.
+
+1. Легко настроить модель или пример под свои нужды:
+ - Мы предоставляем примеры для каждой архитектуры, чтобы воспроизвести результаты, опубликованные их авторами.
+ - Внутренние компоненты модели раскрываются максимально последовательно.
+ - Файлы моделей можно использовать независимо от библиотеки для проведения быстрых экспериментов.
+
+## Почему я не должен использовать transformers?
+
+- Данная библиотека не является модульным набором строительных блоков для нейронных сетей. Код в файлах моделей специально не рефакторится дополнительными абстракциями, чтобы исследователи могли быстро итеративно работать с каждой из моделей, не погружаясь в дополнительные абстракции/файлы.
+- API обучения не предназначен для работы с любой моделью, а оптимизирован для работы с моделями, предоставляемыми библиотекой. Для работы с общими циклами машинного обучения следует использовать другую библиотеку (возможно, [Accelerate](https://huggingface.co/docs/accelerate)).
+- Несмотря на то, что мы стремимся представить как можно больше примеров использования, скрипты в нашей папке [примеров](https://github.com/huggingface/transformers/tree/main/examples) являются именно примерами. Предполагается, что они не будут работать "из коробки" для решения вашей конкретной задачи, и вам придется изменить несколько строк кода, чтобы адаптировать их под свои нужды.
+
+## Установка
+
+### С помощью pip
+
+Данный репозиторий протестирован на Python 3.9+, Flax 0.4.1+, PyTorch 2.1+ и TensorFlow 2.6+.
+
+Устанавливать 🤗 Transformers следует в [виртуальной среде](https://docs.python.org/3/library/venv.html). Если вы не знакомы с виртуальными средами Python, ознакомьтесь с [руководством пользователя](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+Сначала создайте виртуальную среду с той версией Python, которую вы собираетесь использовать, и активируйте ее.
+
+Затем необходимо установить хотя бы один бекенд из Flax, PyTorch или TensorFlow.
+Пожалуйста, обратитесь к страницам [TensorFlow установочная страница](https://www.tensorflow.org/install/), [PyTorch установочная страница](https://pytorch.org/get-started/locally/#start-locally) и/или [Flax](https://github.com/google/flax#quick-install) и [Jax](https://github.com/google/jax#installation), где описаны команды установки для вашей платформы.
+
+После установки одного из этих бэкендов 🤗 Transformers может быть установлен с помощью pip следующим образом:
+
+```bash
+pip install transformers
+```
+
+Если вы хотите поиграть с примерами или вам нужен самый современный код и вы не можете ждать нового релиза, вы должны [установить библиотеку из исходного кода](https://huggingface.co/docs/transformers/installation#installing-from-source).
+
+### С помощью conda
+
+Установить Transformers с помощью conda можно следующим образом:
+
+```bash
+conda install conda-forge::transformers
+```
+
+> **_ЗАМЕТКА:_** Установка `transformers` через канал `huggingface` устарела.
+
+О том, как установить Flax, PyTorch или TensorFlow с помощью conda, читайте на страницах, посвященных их установке.
+
+> **_ЗАМЕТКА:_** В операционной системе Windows вам может быть предложено активировать режим разработчика, чтобы воспользоваться преимуществами кэширования. Если для вас это невозможно, сообщите нам об этом [здесь](https://github.com/huggingface/huggingface_hub/issues/1062).
+
+## Модельные архитектуры
+
+**[Все контрольные точки моделей](https://huggingface.co/models)**, предоставляемые 🤗 Transformers, беспрепятственно интегрируются с huggingface.co [model hub](https://huggingface.co/models), куда они загружаются непосредственно [пользователями](https://huggingface.co/users) и [организациями](https://huggingface.co/organizations).
+
+Текущее количество контрольных точек: 
+
+🤗 В настоящее время Transformers предоставляет следующие архитектуры: подробное описание каждой из них см. [здесь](https://huggingface.co/docs/transformers/model_summary).
+
+Чтобы проверить, есть ли у каждой модели реализация на Flax, PyTorch или TensorFlow, или связанный с ней токенизатор, поддерживаемый библиотекой 🤗 Tokenizers, обратитесь к [этой таблице](https://huggingface.co/docs/transformers/index#supported-frameworks).
+
+Эти реализации были протестированы на нескольких наборах данных (см. примеры скриптов) и должны соответствовать производительности оригинальных реализаций. Более подробную информацию о производительности можно найти в разделе "Примеры" [документации](https://github.com/huggingface/transformers/tree/main/examples).
+
+
+## Изучи больше
+
+| Секция | Описание |
+|-|-|
+| [Документация](https://huggingface.co/docs/transformers/) | Полная документация по API и гайды |
+| [Краткие описания задач](https://huggingface.co/docs/transformers/task_summary) | Задачи поддерживаются 🤗 Transformers |
+| [Пособие по предварительной обработке](https://huggingface.co/docs/transformers/preprocessing) | Использование класса `Tokenizer` для подготовки данных для моделей |
+| [Обучение и доработка](https://huggingface.co/docs/transformers/training) | Использование моделей, предоставляемых 🤗 Transformers, в цикле обучения PyTorch/TensorFlow и API `Trainer`. |
+| [Быстрый тур: Тонкая настройка/скрипты использования](https://github.com/huggingface/transformers/tree/main/examples) | Примеры скриптов для тонкой настройки моделей на широком спектре задач |
+| [Совместное использование и загрузка моделей](https://huggingface.co/docs/transformers/model_sharing) | Загружайте и делитесь с сообществом своими доработанными моделями |
+
+## Цитирование
+
+Теперь у нас есть [статья](https://www.aclweb.org/anthology/2020.emnlp-demos.6/), которую можно цитировать для библиотеки 🤗 Transformers:
+```bibtex
+@inproceedings{wolf-etal-2020-transformers,
+ title = "Transformers: State-of-the-Art Natural Language Processing",
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+ month = oct,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_te.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_te.md
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@@ -0,0 +1,316 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
JAX, PyTorch మరియు TensorFlow కోసం అత్యాధునిక యంత్ర అభ్యాసం
+ + +
+
+
+
+🤗 ట్రాన్స్ఫార్మర్లు టెక్స్ట్, విజన్ మరియు ఆడియో వంటి విభిన్న పద్ధతులపై టాస్క్లను నిర్వహించడానికి వేలాది ముందుగా శిక్షణ పొందిన మోడల్లను అందిస్తాయి.
+
+ఈ నమూనాలు వర్తించవచ్చు:
+
+* 📝 టెక్స్ట్, 100కి పైగా భాషల్లో టెక్స్ట్ క్లాసిఫికేషన్, ఇన్ఫర్మేషన్ ఎక్స్ట్రాక్షన్, ప్రశ్నలకు సమాధానాలు, సారాంశం, అనువాదం, టెక్స్ట్ జనరేషన్ వంటి పనుల కోసం.
+* 🖼️ ఇమేజ్లు, ఇమేజ్ వర్గీకరణ, ఆబ్జెక్ట్ డిటెక్షన్ మరియు సెగ్మెంటేషన్ వంటి పనుల కోసం.
+* 🗣️ ఆడియో, స్పీచ్ రికగ్నిషన్ మరియు ఆడియో వర్గీకరణ వంటి పనుల కోసం.
+
+ట్రాన్స్ఫార్మర్ మోడల్లు టేబుల్ క్వశ్చన్ ఆన్సర్ చేయడం, ఆప్టికల్ క్యారెక్టర్ రికగ్నిషన్, స్కాన్ చేసిన డాక్యుమెంట్ల నుండి ఇన్ఫర్మేషన్ ఎక్స్ట్రాక్షన్, వీడియో క్లాసిఫికేషన్ మరియు విజువల్ క్వశ్చన్ ఆన్సర్ చేయడం వంటి **అనేక పద్ధతులతో కలిపి** పనులను కూడా చేయగలవు.
+
+🤗 ట్రాన్స్ఫార్మర్లు అందించిన టెక్స్ట్లో ప్రీట్రైన్డ్ మోడల్లను త్వరగా డౌన్లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, వాటిని మీ స్వంత డేటాసెట్లలో ఫైన్-ట్యూన్ చేయడానికి మరియు వాటిని మా [మోడల్ హబ్](https://huggingface.co/models)లో సంఘంతో భాగస్వామ్యం చేయడానికి API లను అందిస్తుంది. అదే సమయంలో, ఆర్కిటెక్చర్ని నిర్వచించే ప్రతి పైథాన్ మాడ్యూల్ పూర్తిగా స్వతంత్రంగా ఉంటుంది మరియు త్వరిత పరిశోధన ప్రయోగాలను ప్రారంభించడానికి సవరించవచ్చు.
+
+🤗 ట్రాన్స్ఫార్మర్లకు మూడు అత్యంత ప్రజాదరణ పొందిన డీప్ లెర్నింగ్ లైబ్రరీలు ఉన్నాయి — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) మరియు [TensorFlow](https://www.tensorflow.org/) — వాటి మధ్య అతుకులు లేని ఏకీకరణతో. మీ మోడల్లను ఒకదానితో మరొకదానితో అనుమితి కోసం లోడ్ చేసే ముందు వాటికి శిక్షణ ఇవ్వడం చాలా సులభం.
+
+## ఆన్లైన్ డెమోలు
+
+మీరు [మోడల్ హబ్](https://huggingface.co/models) నుండి మా మోడళ్లలో చాలా వరకు వాటి పేజీలలో నేరుగా పరీక్షించవచ్చు. మేము పబ్లిక్ మరియు ప్రైవేట్ మోడల్ల కోసం [ప్రైవేట్ మోడల్ హోస్టింగ్, సంస్కరణ & అనుమితి API](https://huggingface.co/pricing)ని కూడా అందిస్తాము.
+
+ఇక్కడ కొన్ని ఉదాహరణలు ఉన్నాయి:
+
+సహజ భాషా ప్రాసెసింగ్లో:
+- [BERT తో మాస్క్డ్ వర్డ్ కంప్లీషన్](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Electra తో పేరు ఎంటిటీ గుర్తింపు](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [GPT-2 తో టెక్స్ట్ జనరేషన్](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
+- [RoBERTa తో సహజ భాషా అనుమితి](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+Lost.+Nobody+lost+any+animal)
+- [BART తో సారాంశం](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [DistilBERT తో ప్రశ్న సమాధానం](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [T5 తో అనువాదం](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+కంప్యూటర్ దృష్టిలో:
+- [VIT తో చిత్ర వర్గీకరణ](https://huggingface.co/google/vit-base-patch16-224)
+- [DETR తో ఆబ్జెక్ట్ డిటెక్షన్](https://huggingface.co/facebook/detr-resnet-50)
+- [SegFormer తో సెమాంటిక్ సెగ్మెంటేషన్](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [MaskFormer తో పానోప్టిక్ సెగ్మెంటేషన్](https://huggingface.co/facebook/maskformer-swin-small-coco)
+- [DPT తో లోతు అంచనా](https://huggingface.co/docs/transformers/model_doc/dpt)
+- [VideoMAE తో వీడియో వర్గీకరణ](https://huggingface.co/docs/transformers/model_doc/videomae)
+- [OneFormer తో యూనివర్సల్ సెగ్మెంటేషన్](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+ఆడియోలో:
+- [Wav2Vec2 తో ఆటోమేటిక్ స్పీచ్ రికగ్నిషన్](https://huggingface.co/facebook/wav2vec2-base-960h)
+- [Wav2Vec2 తో కీవర్డ్ స్పాటింగ్](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [ఆడియో స్పెక్ట్రోగ్రామ్ ట్రాన్స్ఫార్మర్తో ఆడియో వర్గీకరణ](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+
+మల్టీమోడల్ టాస్క్లలో:
+- [TAPAS తో టేబుల్ ప్రశ్న సమాధానాలు](https://huggingface.co/google/tapas-base-finetuned-wtq)
+- [ViLT తో దృశ్యమాన ప్రశ్నకు సమాధానం](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [CLIP తో జీరో-షాట్ ఇమేజ్ వర్గీకరణ](https://huggingface.co/openai/clip-vit-large-patch14)
+- [LayoutLM తో డాక్యుమెంట్ ప్రశ్నకు సమాధానం](https://huggingface.co/impira/layoutlm-document-qa)
+- [X-CLIP తో జీరో-షాట్ వీడియో వర్గీకరణ](https://huggingface.co/docs/transformers/model_doc/xclip)
+
+## ట్రాన్స్ఫార్మర్లను ఉపయోగించి 100 ప్రాజెక్టులు
+
+ట్రాన్స్ఫార్మర్లు ప్రీట్రైన్డ్ మోడల్లను ఉపయోగించడానికి టూల్కిట్ కంటే ఎక్కువ: ఇది దాని చుట్టూ నిర్మించిన ప్రాజెక్ట్ల సంఘం మరియు
+హగ్గింగ్ ఫేస్ హబ్. డెవలపర్లు, పరిశోధకులు, విద్యార్థులు, ప్రొఫెసర్లు, ఇంజనీర్లు మరియు ఎవరినైనా అనుమతించేలా ట్రాన్స్ఫార్మర్లను మేము కోరుకుంటున్నాము
+వారి కలల ప్రాజెక్టులను నిర్మించడానికి.
+
+ట్రాన్స్ఫార్మర్ల 100,000 నక్షత్రాలను జరుపుకోవడానికి, మేము స్పాట్లైట్ని ఉంచాలని నిర్ణయించుకున్నాము
+సంఘం, మరియు మేము 100 జాబితాలను కలిగి ఉన్న [awesome-transformers](./awesome-transformers.md) పేజీని సృష్టించాము.
+ట్రాన్స్ఫార్మర్ల పరిసరాల్లో అద్భుతమైన ప్రాజెక్టులు నిర్మించబడ్డాయి.
+
+జాబితాలో భాగమని మీరు విశ్వసించే ప్రాజెక్ట్ను మీరు కలిగి ఉంటే లేదా ఉపయోగిస్తుంటే, దయచేసి దానిని జోడించడానికి PRని తెరవండి!
+
+## మీరు హగ్గింగ్ ఫేస్ టీమ్ నుండి అనుకూల మద్దతు కోసం చూస్తున్నట్లయితే
+
+
+
++ +## త్వరిత పర్యటన + +ఇచ్చిన ఇన్పుట్ (టెక్స్ట్, ఇమేజ్, ఆడియో, ...)పై తక్షణమే మోడల్ను ఉపయోగించడానికి, మేము `pipeline` API ని అందిస్తాము. పైప్లైన్లు ఆ మోడల్ శిక్షణ సమయంలో ఉపయోగించిన ప్రీప్రాసెసింగ్తో కూడిన ప్రీట్రైన్డ్ మోడల్ను సమూహపరుస్తాయి. సానుకూల మరియు ప్రతికూల పాఠాలను వర్గీకరించడానికి పైప్లైన్ను త్వరగా ఎలా ఉపయోగించాలో ఇక్కడ ఉంది: + +```python +>>> from transformers import pipeline + +# Allocate a pipeline for sentiment-analysis +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +రెండవ లైన్ కోడ్ డౌన్లోడ్ మరియు పైప్లైన్ ఉపయోగించే ప్రీట్రైన్డ్ మోడల్ను కాష్ చేస్తుంది, మూడవది ఇచ్చిన టెక్స్ట్పై మూల్యాంకనం చేస్తుంది. ఇక్కడ సమాధానం 99.97% విశ్వాసంతో "పాజిటివ్". + +చాలా పనులు NLPలో కానీ కంప్యూటర్ విజన్ మరియు స్పీచ్లో కూడా ముందుగా శిక్షణ పొందిన `pipeline` సిద్ధంగా ఉన్నాయి. ఉదాహరణకు, మనం చిత్రంలో గుర్తించిన వస్తువులను సులభంగా సంగ్రహించవచ్చు: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Download an image with cute cats +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Allocate a pipeline for object detection +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +ఇక్కడ మనం ఆబ్జెక్ట్ చుట్టూ ఉన్న బాక్స్ మరియు కాన్ఫిడెన్స్ స్కోర్తో చిత్రంలో గుర్తించబడిన వస్తువుల జాబితాను పొందుతాము. ఇక్కడ ఎడమవైపున ఉన్న అసలు చిత్రం, కుడివైపున అంచనాలు ప్రదర్శించబడతాయి: + +
+
+
+
+
+మీరు [ఈ ట్యుటోరియల్](https://huggingface.co/docs/transformers/task_summary)లో `pipeline` API ద్వారా సపోర్ట్ చేసే టాస్క్ల గురించి మరింత తెలుసుకోవచ్చు.
+
+`pipeline`తో పాటు, మీరు ఇచ్చిన టాస్క్లో ఏదైనా ప్రీట్రైన్డ్ మోడల్లను డౌన్లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, దీనికి మూడు లైన్ల కోడ్ సరిపోతుంది. ఇక్కడ PyTorch వెర్షన్ ఉంది:
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+
+మరియు TensorFlow కి సమానమైన కోడ్ ఇక్కడ ఉంది:
+```python
+>>> from transformers import AutoTokenizer, TFAutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="tf")
+>>> outputs = model(**inputs)
+```
+
+ప్రిట్రైన్డ్ మోడల్ ఆశించే అన్ని ప్రీప్రాసెసింగ్లకు టోకెనైజర్ బాధ్యత వహిస్తుంది మరియు నేరుగా ఒకే స్ట్రింగ్ (పై ఉదాహరణలలో వలె) లేదా జాబితాపై కాల్ చేయవచ్చు. ఇది మీరు డౌన్స్ట్రీమ్ కోడ్లో ఉపయోగించగల నిఘంటువుని అవుట్పుట్ చేస్తుంది లేదా ** ఆర్గ్యుమెంట్ అన్ప్యాకింగ్ ఆపరేటర్ని ఉపయోగించి నేరుగా మీ మోడల్కి పంపుతుంది.
+
+మోడల్ కూడా సాధారణ [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) లేదా [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (మీ బ్యాకెండ్ని బట్టి) మీరు మామూలుగా ఉపయోగించవచ్చు. [ఈ ట్యుటోరియల్](https://huggingface.co/docs/transformers/training) అటువంటి మోడల్ని క్లాసిక్ PyTorch లేదా TensorFlow ట్రైనింగ్ లూప్లో ఎలా ఇంటిగ్రేట్ చేయాలో లేదా మా `Trainer` API ని ఎలా ఉపయోగించాలో వివరిస్తుంది కొత్త డేటాసెట్.
+
+## నేను ట్రాన్స్ఫార్మర్లను ఎందుకు ఉపయోగించాలి?
+
+1. ఉపయోగించడానికి సులభమైన స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్లు:
+ - సహజ భాషా అవగాహన & ఉత్పత్తి, కంప్యూటర్ దృష్టి మరియు ఆడియో పనులపై అధిక పనితీరు.
+ - విద్యావేత్తలు మరియు అభ్యాసకుల ప్రవేశానికి తక్కువ అవరోధం.
+ - తెలుసుకోవడానికి కేవలం మూడు తరగతులతో కొన్ని వినియోగదారు-ముఖ సంగ్రహణలు.
+ - మా అన్ని ప్రీట్రైన్డ్ మోడల్లను ఉపయోగించడం కోసం ఏకీకృత API.
+
+2. తక్కువ గణన ఖర్చులు, చిన్న కార్బన్ పాదముద్ర:
+ - పరిశోధకులు ఎల్లప్పుడూ మళ్లీ శిక్షణ పొందే బదులు శిక్షణ పొందిన నమూనాలను పంచుకోవచ్చు.
+ - అభ్యాసకులు గణన సమయాన్ని మరియు ఉత్పత్తి ఖర్చులను తగ్గించగలరు.
+ - అన్ని పద్ధతుల్లో 60,000 కంటే ఎక్కువ ప్రీట్రైన్డ్ మోడల్లతో డజన్ల కొద్దీ ఆర్కిటెక్చర్లు.
+
+3. మోడల్ జీవితకాలంలో ప్రతి భాగానికి సరైన ఫ్రేమ్వర్క్ను ఎంచుకోండి:
+ - 3 లైన్ల కోడ్లో స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్లకు శిక్షణ ఇవ్వండి.
+ - TF2.0/PyTorch/JAX ఫ్రేమ్వర్క్ల మధ్య ఒకే మోడల్ను ఇష్టానుసారంగా తరలించండి.
+ - శిక్షణ, మూల్యాంకనం మరియు ఉత్పత్తి కోసం సరైన ఫ్రేమ్వర్క్ను సజావుగా ఎంచుకోండి.
+
+4. మీ అవసరాలకు అనుగుణంగా మోడల్ లేదా ఉదాహరణను సులభంగా అనుకూలీకరించండి:
+ - ప్రతి ఆర్కిటెక్చర్ దాని అసలు రచయితలు ప్రచురించిన ఫలితాలను పునరుత్పత్తి చేయడానికి మేము ఉదాహరణలను అందిస్తాము.
+ - మోడల్ ఇంటర్నల్లు వీలైనంత స్థిరంగా బహిర్గతమవుతాయి.
+ - శీఘ్ర ప్రయోగాల కోసం లైబ్రరీ నుండి స్వతంత్రంగా మోడల్ ఫైల్లను ఉపయోగించవచ్చు.
+
+## నేను ట్రాన్స్ఫార్మర్లను ఎందుకు ఉపయోగించకూడదు?
+
+- ఈ లైబ్రరీ న్యూరల్ నెట్ల కోసం బిల్డింగ్ బ్లాక్ల మాడ్యులర్ టూల్బాక్స్ కాదు. మోడల్ ఫైల్లలోని కోడ్ ఉద్దేశపూర్వకంగా అదనపు సంగ్రహణలతో రీఫ్యాక్టరింగ్ చేయబడదు, తద్వారా పరిశోధకులు అదనపు సంగ్రహణలు/ఫైళ్లలోకి ప్రవేశించకుండా ప్రతి మోడల్పై త్వరగా మళ్లించగలరు.
+- శిక్షణ API ఏ మోడల్లో పని చేయడానికి ఉద్దేశించబడలేదు కానీ లైబ్రరీ అందించిన మోడల్లతో పని చేయడానికి ఆప్టిమైజ్ చేయబడింది. సాధారణ మెషిన్ లెర్నింగ్ లూప్ల కోసం, మీరు మరొక లైబ్రరీని ఉపయోగించాలి (బహుశా, [Accelerate](https://huggingface.co/docs/accelerate)).
+- మేము వీలైనన్ని ఎక్కువ వినియోగ సందర్భాలను ప్రదర్శించడానికి ప్రయత్నిస్తున్నప్పుడు, మా [ఉదాహరణల ఫోల్డర్](https://github.com/huggingface/transformers/tree/main/examples)లోని స్క్రిప్ట్లు కేవలం: ఉదాహరణలు. మీ నిర్దిష్ట సమస్యపై అవి పని చేయవు మరియు వాటిని మీ అవసరాలకు అనుగుణంగా మార్చుకోవడానికి మీరు కొన్ని కోడ్ లైన్లను మార్చవలసి ఉంటుంది.
+
+## సంస్థాపన
+
+### పిప్ తో
+
+ఈ రిపోజిటరీ పైథాన్ 3.9+, ఫ్లాక్స్ 0.4.1+, PyTorch 2.1+ మరియు TensorFlow 2.6+లో పరీక్షించబడింది.
+
+మీరు [వర్చువల్ వాతావరణం](https://docs.python.org/3/library/venv.html)లో 🤗 ట్రాన్స్ఫార్మర్లను ఇన్స్టాల్ చేయాలి. మీకు పైథాన్ వర్చువల్ పరిసరాల గురించి తెలియకుంటే, [యూజర్ గైడ్](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) చూడండి.
+
+ముందుగా, మీరు ఉపయోగించబోతున్న పైథాన్ వెర్షన్తో వర్చువల్ వాతావరణాన్ని సృష్టించండి మరియు దానిని సక్రియం చేయండి.
+
+అప్పుడు, మీరు ఫ్లాక్స్, పైటార్చ్ లేదా టెన్సర్ఫ్లోలో కనీసం ఒకదానిని ఇన్స్టాల్ చేయాలి.
+దయచేసి [TensorFlow ఇన్స్టాలేషన్ పేజీ](https://www.tensorflow.org/install/), [PyTorch ఇన్స్టాలేషన్ పేజీ](https://pytorch.org/get-started/locally/#start-locally) మరియు/ని చూడండి లేదా మీ ప్లాట్ఫారమ్ కోసం నిర్దిష్ట ఇన్స్టాలేషన్ కమాండ్కు సంబంధించి [Flax](https://github.com/google/flax#quick-install) మరియు [Jax](https://github.com/google/jax#installation) ఇన్స్టాలేషన్ పేజీలు .
+
+ఆ బ్యాకెండ్లలో ఒకటి ఇన్స్టాల్ చేయబడినప్పుడు, 🤗 ట్రాన్స్ఫార్మర్లను ఈ క్రింది విధంగా పిప్ని ఉపయోగించి ఇన్స్టాల్ చేయవచ్చు:
+
+```bash
+pip install transformers
+```
+
+మీరు ఉదాహరణలతో ప్లే చేయాలనుకుంటే లేదా కోడ్ యొక్క బ్లీడింగ్ ఎడ్జ్ అవసరం మరియు కొత్త విడుదల కోసం వేచి ఉండలేకపోతే, మీరు తప్పనిసరిగా [మూలం నుండి లైబ్రరీని ఇన్స్టాల్ చేయాలి](https://huggingface.co/docs/transformers/installation#installing-from-source).
+
+### కొండా తో
+
+🤗 కింది విధంగా కొండా ఉపయోగించి ట్రాన్స్ఫార్మర్లను ఇన్స్టాల్ చేయవచ్చు:
+
+```shell script
+conda install conda-forge::transformers
+```
+
+> **_గమనిక:_** `huggingface` ఛానెల్ నుండి `transformers` ఇన్స్టాల్ చేయడం పురాతనంగా ఉంది.
+
+Flax, PyTorch లేదా TensorFlow యొక్క ఇన్స్టాలేషన్ పేజీలను కొండాతో ఎలా ఇన్స్టాల్ చేయాలో చూడటానికి వాటిని అనుసరించండి.
+
+> **_గమనిక:_** Windowsలో, కాషింగ్ నుండి ప్రయోజనం పొందేందుకు మీరు డెవలపర్ మోడ్ని సక్రియం చేయమని ప్రాంప్ట్ చేయబడవచ్చు. ఇది మీకు ఎంపిక కాకపోతే, దయచేసి [ఈ సంచిక](https://github.com/huggingface/huggingface_hub/issues/1062)లో మాకు తెలియజేయండి.
+
+## మోడల్ ఆర్కిటెక్చర్లు
+
+**[అన్ని మోడల్ చెక్పాయింట్లు](https://huggingface.co/models)** 🤗 అందించిన ట్రాన్స్ఫార్మర్లు huggingface.co [model hub](https://huggingface.co/models) నుండి సజావుగా ఏకీకృతం చేయబడ్డాయి [users](https://huggingface.co/users) మరియు [organizations](https://huggingface.co/organizations) ద్వారా నేరుగా అప్లోడ్ చేయబడతాయి.
+
+ప్రస్తుత తనిఖీ కేంద్రాల సంఖ్య: 
+
+🤗 ట్రాన్స్ఫార్మర్లు ప్రస్తుతం కింది ఆర్కిటెక్చర్లను అందజేస్తున్నాయి: వాటిలో ప్రతి ఒక్కటి ఉన్నత స్థాయి సారాంశం కోసం [ఇక్కడ](https://huggingface.co/docs/transformers/model_summary) చూడండి.
+
+ఈ అమలులు అనేక డేటాసెట్లలో పరీక్షించబడ్డాయి (ఉదాహరణ స్క్రిప్ట్లను చూడండి) మరియు అసలైన అమలుల పనితీరుతో సరిపోలాలి. మీరు [డాక్యుమెంటేషన్](https://github.com/huggingface/transformers/tree/main/examples) యొక్క ఉదాహరణల విభాగంలో పనితీరుపై మరిన్ని వివరాలను కనుగొనవచ్చు.
+
+## ఇంకా నేర్చుకో
+
+| విభాగం | వివరణ |
+|-|-|
+| [డాక్యుమెంటేషన్](https://huggingface.co/docs/transformers/) | పూర్తి API డాక్యుమెంటేషన్ మరియు ట్యుటోరియల్స్ |
+| [టాస్క్ సారాంశం](https://huggingface.co/docs/transformers/task_summary) | 🤗 ట్రాన్స్ఫార్మర్ల ద్వారా సపోర్ట్ చేయబడిన విధులు |
+| [ప్రీప్రాసెసింగ్ ట్యుటోరియల్](https://huggingface.co/docs/transformers/preprocessing) | మోడల్ల కోసం డేటాను సిద్ధం చేయడానికి `Tokenizer` క్లాస్ని ఉపయోగించడం |
+| [ట్రైనింగ్ మరియు ఫైన్-ట్యూనింగ్](https://huggingface.co/docs/transformers/training) | PyTorch/TensorFlow ట్రైనింగ్ లూప్ మరియు `Trainer` APIలో 🤗 ట్రాన్స్ఫార్మర్లు అందించిన మోడల్లను ఉపయోగించడం |
+| [త్వరిత పర్యటన: ఫైన్-ట్యూనింగ్/యూసేజ్ స్క్రిప్ట్లు](https://github.com/huggingface/transformers/tree/main/examples) | విస్తృత శ్రేణి టాస్క్లపై ఫైన్-ట్యూనింగ్ మోడల్స్ కోసం ఉదాహరణ స్క్రిప్ట్లు |
+| [మోడల్ భాగస్వామ్యం మరియు అప్లోడ్ చేయడం](https://huggingface.co/docs/transformers/model_sharing) | కమ్యూనిటీతో మీ ఫైన్-ట్యూన్డ్ మోడల్లను అప్లోడ్ చేయండి మరియు భాగస్వామ్యం చేయండి |
+
+## అనులేఖనం
+
+🤗 ట్రాన్స్ఫార్మర్స్ లైబ్రరీ కోసం మీరు ఉదహరించగల [పేపర్](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) ఇప్పుడు మా వద్ద ఉంది:
+```bibtex
+@inproceedings{wolf-etal-2020-transformers,
+ title = "Transformers: State-of-the-Art Natural Language Processing",
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+ month = oct,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_ur.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_ur.md
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@@ -0,0 +1,333 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
جدید ترین مشین لرننگ برائے JAX، PyTorch اور TensorFlow
+ + +
+
+
+
+🤗 Transformers مختلف طریقوں جیسے کہ متن، بصارت، اور آڈیو پر کام کرنے کے لیے ہزاروں پری ٹرینڈ ماڈلز فراہم کرتے ہیں۔
+
+یہ ماڈلز درج ذیل پر لاگو کیے جا سکتے ہیں:
+
+* 📝 متن، جیسے کہ متن کی درجہ بندی، معلومات کا استخراج، سوالات کے جوابات، خلاصہ، ترجمہ، اور متن کی تخلیق، 100 سے زائد زبانوں میں۔
+* 🖼️ تصاویر، جیسے کہ تصویر کی درجہ بندی، اشیاء کی شناخت، اور تقسیم۔
+* 🗣️ آڈیو، جیسے کہ تقریر کی شناخت اور آڈیو کی درجہ بندی۔
+
+ٹرانسفارمر ماڈلز **مختلف طریقوں کو ملا کر** بھی کام انجام دے سکتے ہیں، جیسے کہ ٹیبل سوال جواب، بصری حروف کی شناخت، اسکین شدہ دستاویزات سے معلومات نکالنا، ویڈیو کی درجہ بندی، اور بصری سوال جواب۔
+
+🤗 Transformers ایسے APIs فراہم کرتا ہے جو آپ کو تیز رفتاری سے پری ٹرینڈ ماڈلز کو ایک دیے گئے متن پر ڈاؤن لوڈ اور استعمال کرنے، انہیں اپنے ڈیٹا سیٹس پر فائن ٹون کرنے، اور پھر ہمارے [ماڈل حب](https://huggingface.co/models) پر کمیونٹی کے ساتھ شیئر کرنے کی سہولت دیتا ہے۔ اسی وقت، ہر پائتھن ماڈیول جو ایک آرکیٹیکچر کو بیان کرتا ہے، مکمل طور پر خود مختار ہوتا ہے اور اسے تیز تحقیقاتی تجربات کے لیے تبدیل کیا جا سکتا ہے۔
+
+
+🤗 Transformers تین سب سے مشہور ڈیپ لرننگ لائبریریوں — [Jax](https://jax.readthedocs.io/en/latest/)، [PyTorch](https://pytorch.org/) اور [TensorFlow](https://www.tensorflow.org/) — کی مدد سے تیار کردہ ہے، جن کے درمیان بے حد ہموار انضمام ہے۔ اپنے ماڈلز کو ایک کے ساتھ تربیت دینا اور پھر دوسرے کے ساتھ inference کے لیے لوڈ کرنا انتہائی سادہ ہے۔
+
+## آن لائن ڈیمو
+
+آپ ہمارے زیادہ تر ماڈلز کو براہ راست ان کے صفحات پر [ماڈل ہب](https://huggingface.co/models) سے آزما سکتے ہیں۔ ہم عوامی اور نجی ماڈلز کے لیے [ذاتی ماڈل ہوسٹنگ، ورژننگ، اور انفرنس API](https://huggingface.co/pricing) بھی فراہم کرتے ہیں۔
+
+یہاں چند مثالیں ہیں:
+
+قدرتی زبان کی پروسیسنگ میں:
+
+- [BERT کے ساتھ ماسک شدہ الفاظ کی تکمیل](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Electra کے ساتھ نامزد اداروں کی شناخت](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [Mistral کے ساتھ متنی جنریشن](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+- [RoBERTa کے ساتھ قدرتی زبان کی دلیل](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [BART کے ساتھ خلاصہ کاری](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [DistilBERT کے ساتھ سوالات کے جوابات](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [T5 کے ساتھ ترجمہ](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+کمپیوٹر وژن میں:
+- [ViT کے ساتھ امیج کی درجہ بندی](https://huggingface.co/google/vit-base-patch16-224)
+- [DETR کے ساتھ اشیاء کی شناخت](https://huggingface.co/facebook/detr-resnet-50)
+- [SegFormer کے ساتھ سیمانٹک سیگمینٹیشن](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [Mask2Former کے ساتھ پینوسٹک سیگمینٹیشن](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic)
+- [Depth Anything کے ساتھ گہرائی کا اندازہ](https://huggingface.co/docs/transformers/main/model_doc/depth_anything)
+- [VideoMAE کے ساتھ ویڈیو کی درجہ بندی](https://huggingface.co/docs/transformers/model_doc/videomae)
+- [OneFormer کے ساتھ یونیورسل سیگمینٹیشن](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+
+آڈیو:
+- [خودکار تقریر کی پہچان Whisper کے ساتھ](https://huggingface.co/openai/whisper-large-v3)
+- [کلیدی الفاظ کی تلاش Wav2Vec2 کے ساتھ](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [آڈیو کی درجہ بندی Audio Spectrogram Transformer کے ساتھ](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+
+ملٹی ماڈل ٹاسک میں:
+
+- [ٹیبل سوال جواب کے لیے TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
+- [ویژول سوال جواب کے لیے ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [امیج کیپشننگ کے لیے LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf)
+- [زیرو شاٹ امیج کلاسیفیکیشن کے لیے SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384)
+- [دستاویزی سوال جواب کے لیے LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
+- [زیرو شاٹ ویڈیو کلاسیفیکیشن کے لیے X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
+- [زیرو شاٹ آبجیکٹ ڈیٹیکشن کے لیے OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2)
+- [زیرو شاٹ امیج سیگمنٹیشن کے لیے CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)
+- [خودکار ماسک جنریشن کے لیے SAM](https://huggingface.co/docs/transformers/model_doc/sam)
+
+
+## ٹرانسفارمرز کے 100 منصوبے
+
+🤗 Transformers صرف پیشگی تربیت یافتہ ماڈلز کا ایک ٹول کٹ نہیں ہے: یہ ایک کمیونٹی ہے جو اس کے ارد گرد اور ہیگنگ فیس حب پر تعمیر شدہ منصوبوں کا مجموعہ ہے۔ ہم چاہتے ہیں کہ🤗 Transformers ترقی کاروں، محققین، طلباء، پروفیسرز، انجینئرز، اور ہر کسی کو اپنے خوابوں کے منصوبے بنانے میں مدد فراہم کرے۔
+
+
+🤗 Transformers کے 100,000 ستاروں کی خوشی منانے کے لیے، ہم نے کمیونٹی پر روشنی ڈالنے کا فیصلہ کیا ہے، اور ہم نے [awesome-transformers](./awesome-transformers.md) کا صفحہ بنایا ہے جو 100 شاندار منصوبے درج کرتا ہے جو 🤗 Transformers کے ارد گرد بنائے گئے ہیں۔
+
+اگر آپ کے پاس کوئی ایسا منصوبہ ہے جسے آپ سمجھتے ہیں کہ اس فہرست کا حصہ ہونا چاہیے، تو براہ کرم ایک PR کھولیں تاکہ اسے شامل کیا جا سکے!
+
+## اگر آپ ہیگنگ فیس ٹیم سے حسب ضرورت معاونت تلاش کر رہے ہیں
+
+
+
++ +## فوری ٹور + +دیے گئے ان پٹ (متن، تصویر، آڈیو، ...) پر ماڈل کو فوری طور پر استعمال کرنے کے لیے، ہم pipeline API فراہم کرتے ہیں۔ پائپ لائنز ایک پیشگی تربیت یافتہ ماڈل کو اس ماڈل کی تربیت کے دوران استعمال ہونے والے پری پروسیسنگ کے ساتھ گروپ کرتی ہیں۔ یہاں یہ ہے کہ مثبت اور منفی متون کی درجہ بندی کے لیے پائپ لائن کو جلدی سے کیسے استعمال کیا جائے: + + +```python +>>> from transformers import pipeline + +# جذبات کے تجزیے کے لیے ایک پائپ لائن مختص کریں +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +دوسری لائن کوڈ پائپ لائن کے ذریعہ استعمال ہونے والے پیشگی تربیت یافتہ ماڈل کو ڈاؤن لوڈ اور کیش کرتی ہے، جبکہ تیسری لائن اسے دیے گئے متن پر جانچتی ہے۔ یہاں، جواب "مثبت" ہے جس کی اعتماد کی شرح 99.97% ہے۔ + +بہت سے کاموں کے لیے ایک پیشگی تربیت یافتہ pipeline تیار ہے، NLP کے علاوہ کمپیوٹر ویژن اور آواز میں بھی۔ مثال کے طور پر، ہم تصویر میں دریافت شدہ اشیاء کو آسانی سے نکال سکتے ہیں: + + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# جذبات کے تجزیے کے لیے ایک پائپ لائن مختص کریں +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621، + 'label': 'remote'، + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}، + {'score': 0.9960021376609802، + 'label': 'remote'، + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}، + {'score': 0.9954745173454285، + 'label': 'couch'، + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}، + {'score': 0.9988006353378296، + 'label': 'cat'، + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}، + {'score': 0.9986783862113953، + 'label': 'cat'، + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +یہاں، ہم کو تصویر میں دریافت شدہ اشیاء کی فہرست ملتی ہے، ہر ایک کے گرد ایک باکس اور اعتماد کا اسکور۔ یہاں اصل تصویر بائیں طرف ہے، اور پیشگوئیاں دائیں طرف ظاہر کی گئی ہیں: + + +
+
+
+
+
+آپ `pipeline` API کی مدد سے معاونت شدہ کاموں کے بارے میں مزید جان سکتے ہیں [اس ٹیوٹوریل](https://huggingface.co/docs/transformers/task_summary) میں۔
+
+
+`pipeline` کے علاوہ، کسی بھی پیشگی تربیت یافتہ ماڈل کو آپ کے دیے گئے کام پر ڈاؤن لوڈ اور استعمال کرنے کے لیے، صرف تین لائنوں کا کوڈ کافی ہے۔ یہاں PyTorch ورژن ہے:
+
+```python
+>>> from transformers import AutoTokenizer، AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!"، return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+
+اور یہاں TensorFlow کے لیے مساوی کوڈ ہے:
+```python
+>>> from transformers import AutoTokenizer، TFAutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!"، return_tensors="tf")
+>>> outputs = model(**inputs)
+```
+
+ٹوکینائزر تمام پری پروسیسنگ کا ذمہ دار ہے جس کی پیشگی تربیت یافتہ ماڈل کو ضرورت ہوتی ہے اور اسے براہ راست ایک واحد سٹرنگ (جیسا کہ اوپر کی مثالوں میں) یا ایک فہرست پر کال کیا جا سکتا ہے۔ یہ ایک لغت فراہم کرے گا جسے آپ ڈاؤن اسٹریم کوڈ میں استعمال کر سکتے ہیں یا سادہ طور پر اپنے ماڈل کو ** دلیل انپیکنگ آپریٹر کے ذریعے براہ راست پاس کر سکتے ہیں۔
+
+ماڈل خود ایک باقاعدہ [PyTorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) یا [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (آپ کے بیک اینڈ پر منحصر ہے) ہے جسے آپ معمول کے مطابق استعمال کر سکتے ہیں۔ [یہ ٹیوٹوریل](https://huggingface.co/docs/transformers/training) وضاحت کرتا ہے کہ کلاسیکی PyTorch یا TensorFlow تربیتی لوپ میں ایسے ماڈل کو کیسے ضم کیا جائے، یا ہمارے `Trainer` API کا استعمال کرتے ہوئے نئے ڈیٹا سیٹ پر جلدی سے فائن ٹیون کیسے کیا جائے۔
+
+## مجھے Transformers کیوں استعمال کرنا چاہیے؟
+
+ 1. استعمال میں آسان جدید ترین ماڈلز:
+
+ - قدرتی زبان کی سمجھ اور تخلیق، کمپیوٹر وژن، اور آڈیو کے کاموں میں اعلی کارکردگی۔
+ - معلمین اور عملی ماہرین کے لیے کم داخلی رکاوٹ۔
+ - سیکھنے کے لیے صرف تین کلاسز کے ساتھ چند یوزر فرینڈلی ایبسٹریکشنز۔
+ - ہمارے تمام pretrained ماڈلز کے استعمال کے لیے ایک متحد API۔
+
+ 2. کمپیوٹیشن کے اخراجات میں کمی، کاربن فٹ پرنٹ میں کمی:
+
+- محققین ہمیشہ دوبارہ تربیت کرنے کی بجائے تربیت شدہ ماڈلز شیئر کر سکتے ہیں۔
+- عملی ماہرین کمپیوٹ وقت اور پروڈکشن اخراجات کو کم کر سکتے ہیں۔
+- ہر موڈیلٹی کے لیے 400,000 سے زیادہ pretrained ماڈلز کے ساتھ درجنوں آرکیٹیکچرز۔
+
+ 3. ماڈل کے لائف ٹائم کے ہر حصے کے لیے صحیح
+فریم ورک کا انتخاب کریں:
+
+ - 3 لائنز کے کوڈ میں جدید ترین ماڈلز تربیت دیں۔
+ - ایک ماڈل کو کسی بھی وقت TF2.0/PyTorch/JAX فریم ورکس کے درمیان منتقل کریں۔
+ - تربیت، تشخیص، اور پروڈکشن کے لیے بغیر کسی رکاوٹ کے صحیح فریم ورک کا انتخاب کریں۔
+
+ 4. اپنے ضروریات کے مطابق آسانی سے ماڈل یا ایک مثال کو حسب ضرورت بنائیں:
+
+ - ہم ہر آرکیٹیکچر کے لیے مثالیں فراہم کرتے ہیں تاکہ اصل مصنفین کے شائع شدہ نتائج کو دوبارہ پیدا کیا جا سکے۔
+ - ماڈلز کی اندرونی تفصیلات کو جتنا ممکن ہو یکساں طور پر ظاہر کیا جاتا ہے۔
+ - فوری تجربات کے لیے ماڈل فائلز کو لائبریری سے آزادانہ طور پر استعمال کیا جا سکتا ہے۔
+
+## مجھے Transformers کیوں استعمال نہیں کرنا چاہیے؟
+
+- یہ لائبریری نیورل نیٹس کے لیے بلڈنگ بلاکس کا ماڈیولر ٹول باکس نہیں ہے۔ ماڈل فائلز میں موجود کوڈ جان بوجھ کر اضافی ایبسٹریکشنز کے ساتھ دوبارہ ترتیب نہیں دیا گیا ہے، تاکہ محققین بغیر اضافی ایبسٹریکشنز/فائلوں میں گئے ہوئے جلدی سے ہر ماڈل پر کام کر سکیں۔
+- تربیتی API کا مقصد کسی بھی ماڈل پر کام کرنے کے لیے نہیں ہے بلکہ یہ لائبریری کے فراہم کردہ ماڈلز کے ساتھ کام کرنے کے لیے بہتر بنایا گیا ہے۔ عام مشین لرننگ لوپس کے لیے، آپ کو دوسری لائبریری (ممکنہ طور پر [Accelerate](https://huggingface.co/docs/accelerate)) استعمال کرنی چاہیے۔
+- حالانکہ ہم جتنا ممکن ہو زیادہ سے زیادہ استعمال کے کیسز پیش کرنے کی کوشش کرتے ہیں، ہمارے [مثالوں کے فولڈر](https://github.com/huggingface/transformers/tree/main/examples) میں موجود اسکرپٹس صرف یہی ہیں: مثالیں۔ یہ توقع کی جاتی ہے کہ یہ آپ کے مخصوص مسئلے پر فوراً کام نہیں کریں گی اور آپ کو اپنی ضروریات کے مطابق کوڈ کی کچھ لائنیں تبدیل کرنی پڑیں گی۔
+
+### انسٹالیشن
+
+#### pip کے ساتھ
+
+یہ ریپوزٹری Python 3.9+، Flax 0.4.1+، PyTorch 2.1+، اور TensorFlow 2.6+ پر ٹیسٹ کی گئی ہے۔
+
+آپ کو 🤗 Transformers کو ایک [ورچوئل ماحول](https://docs.python.org/3/library/venv.html) میں انسٹال کرنا چاہیے۔ اگر آپ Python ورچوئل ماحول سے واقف نہیں ہیں، تو [یوزر گائیڈ](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) دیکھیں۔
+
+پہلے، Python کے اس ورژن کے ساتھ ایک ورچوئل ماحول بنائیں جو آپ استعمال کر رہے ہیں اور اسے ایکٹیویٹ کریں۔
+
+پھر، آپ کو کم از کم Flax، PyTorch، یا TensorFlow میں سے کسی ایک کو انسٹال کرنے کی ضرورت ہوگی۔
+براہ کرم اپنے پلیٹ فارم کے لیے مخصوص انسٹالیشن کمانڈ کے حوالے سے [TensorFlow انسٹالیشن صفحہ](https://www.tensorflow.org/install/)، [PyTorch انسٹالیشن صفحہ](https://pytorch.org/get-started/locally/#start-locally) اور/یا [Flax](https://github.com/google/flax#quick-install) اور [Jax](https://github.com/google/jax#installation) انسٹالیشن صفحات دیکھیں۔
+
+جب ان میں سے کوئی ایک بیک اینڈ انسٹال ہو جائے، تو 🤗 Transformers کو pip کے ذریعے مندرجہ ذیل طریقے سے انسٹال کیا جا سکتا ہے:
+
+```bash
+pip install transformers
+```
+
+اگر آپ مثالوں کے ساتھ کھیلنا چاہتے ہیں یا آپ کو کوڈ کا تازہ ترین ورژن چاہیے اور آپ نئے ریلیز کا انتظار نہیں کر سکتے، تو آپ کو [سورس سے لائبریری انسٹال کرنی ہوگی](https://huggingface.co/docs/transformers/installation#installing-from-source)۔
+
+#### conda کے ساتھ
+
+🤗 Transformers کو conda کے ذریعے مندرجہ ذیل طریقے سے انسٹال کیا جا سکتا ہے:
+
+```shell script
+conda install conda-forge::transformers
+```
+
+> **_نوٹ:_** `transformers` کو `huggingface` چینل سے انسٹال کرنا اب ختم کیا جا چکا ہے۔
+
+Flax، PyTorch، یا TensorFlow کو conda کے ساتھ انسٹال کرنے کے لیے انسٹالیشن صفحات کی پیروی کریں۔
+
+> **_نوٹ:_** ونڈوز پر، آپ کو کیشنگ سے فائدہ اٹھانے کے لیے ڈویلپر موڈ کو ایکٹیویٹ کرنے کا پیغام دیا جا سکتا ہے۔ اگر یہ آپ کے لیے ممکن نہیں ہے، تو براہ کرم ہمیں [اس مسئلے](https://github.com/huggingface/huggingface_hub/issues/1062) میں بتائیں۔
+
+### ماڈل کی تعمیرات
+
+ 🤗 Transformers کی طرف سے فراہم کردہ **[تمام ماڈل چیک پوائنٹس](https://huggingface.co/models)** ہگنگ فیس کے ماڈل حب [model hub](https://huggingface.co/models) سے بآسانی مربوط ہیں، جہاں یہ براہ راست [صارفین](https://huggingface.co/users) اور [تنظیموں](https://huggingface.co/organizations) کے ذریعہ اپ لوڈ کیے جاتے ہیں۔
+
+چیک پوائنٹس کی موجودہ تعداد: 
+
+🤗 Transformers فی الحال درج ذیل معماریاں فراہم کرتا ہے: ہر ایک کا اعلی سطحی خلاصہ دیکھنے کے لیے [یہاں](https://huggingface.co/docs/transformers/model_summary) دیکھیں۔
+
+یہ چیک کرنے کے لیے کہ ہر ماڈل کی Flax، PyTorch یا TensorFlow میں کوئی عملداری ہے یا 🤗 Tokenizers لائبریری کے ذریعہ سپورٹ کردہ ٹوکنائزر کے ساتھ ہے، [اس جدول](https://huggingface.co/docs/transformers/index#supported-frameworks) کا حوالہ لیں۔
+
+یہ عملداری مختلف ڈیٹا سیٹس پر ٹیسٹ کی گئی ہیں (مثال کے اسکرپٹس دیکھیں) اور اصل عملداری کی کارکردگی کے ہم آہنگ ہونی چاہئیں۔ آپ کو کارکردگی کی مزید تفصیلات [دستاویزات](https://github.com/huggingface/transformers/tree/main/examples) کے مثالوں کے سیکشن میں مل سکتی ہیں۔
+
+
+## مزید معلومات حاصل کریں
+
+| سیکشن | تفصیل |
+|-|-|
+| [دستاویزات](https://huggingface.co/docs/transformers/) | مکمل API دستاویزات اور ٹیوٹوریلز |
+| [ٹاسک کا خلاصہ](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers کے ذریعہ سپورٹ کردہ ٹاسک |
+| [پری پروسیسنگ ٹیوٹوریل](https://huggingface.co/docs/transformers/preprocessing) | ماڈلز کے لیے ڈیٹا تیار کرنے کے لیے `Tokenizer` کلاس کا استعمال |
+| [ٹریننگ اور فائن ٹیوننگ](https://huggingface.co/docs/transformers/training) | PyTorch/TensorFlow ٹریننگ لوپ میں 🤗 Transformers کی طرف سے فراہم کردہ ماڈلز کا استعمال اور `Trainer` API |
+| [تیز دورہ: فائن ٹیوننگ/استعمال کے اسکرپٹس](https://github.com/huggingface/transformers/tree/main/examples) | مختلف قسم کے ٹاسک پر ماڈلز کو فائن ٹیون کرنے کے لیے مثال کے اسکرپٹس |
+| [ماڈل کا اشتراک اور اپ لوڈ کرنا](https://huggingface.co/docs/transformers/model_sharing) | اپنی فائن ٹیون کردہ ماڈلز کو کمیونٹی کے ساتھ اپ لوڈ اور شیئر کریں |
+
+## استشہاد
+
+ہم نے اب ایک [تحقیقی مقالہ](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) تیار کیا ہے جسے آپ 🤗 Transformers لائبریری کے لیے حوالہ دے سکتے ہیں:
+
+```bibtex
+@inproceedings{wolf-etal-2020-transformers،
+ title = "Transformers: State-of-the-Art Natural Language Processing"،
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and R{\'e}mi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush"،
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations"،
+ month = oct،
+ year = "2020"،
+ address = "Online"،
+ publisher = "Association for Computational Linguistics"،
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6"،
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_vi.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_vi.md
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@@ -0,0 +1,318 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng việt | + العربية | + اردو | +
+ + ++
Công nghệ Học máy tiên tiến cho JAX, PyTorch và TensorFlow
+ + +
+
+
+
+🤗 Transformers cung cấp hàng ngàn mô hình được huấn luyện trước để thực hiện các nhiệm vụ trên các modalities khác nhau như văn bản, hình ảnh và âm thanh.
+
+Các mô hình này có thể được áp dụng vào:
+
+* 📝 Văn bản, cho các nhiệm vụ như phân loại văn bản, trích xuất thông tin, trả lời câu hỏi, tóm tắt, dịch thuật và sinh văn bản, trong hơn 100 ngôn ngữ.
+* 🖼️ Hình ảnh, cho các nhiệm vụ như phân loại hình ảnh, nhận diện đối tượng và phân đoạn.
+* 🗣️ Âm thanh, cho các nhiệm vụ như nhận dạng giọng nói và phân loại âm thanh.
+
+Các mô hình Transformer cũng có thể thực hiện các nhiệm vụ trên **nhiều modalities kết hợp**, như trả lời câu hỏi về bảng, nhận dạng ký tự quang học, trích xuất thông tin từ tài liệu quét, phân loại video và trả lời câu hỏi hình ảnh.
+
+🤗 Transformers cung cấp các API để tải xuống và sử dụng nhanh chóng các mô hình được huấn luyện trước đó trên văn bản cụ thể, điều chỉnh chúng trên tập dữ liệu của riêng bạn và sau đó chia sẻ chúng với cộng đồng trên [model hub](https://huggingface.co/models) của chúng tôi. Đồng thời, mỗi module python xác định một kiến trúc là hoàn toàn độc lập và có thể được sửa đổi để cho phép thực hiện nhanh các thí nghiệm nghiên cứu.
+
+🤗 Transformers được hỗ trợ bởi ba thư viện học sâu phổ biến nhất — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) và [TensorFlow](https://www.tensorflow.org/) — với tích hợp mượt mà giữa chúng. Việc huấn luyện mô hình của bạn với một thư viện trước khi tải chúng để sử dụng trong suy luận với thư viện khác là rất dễ dàng.
+
+## Các demo trực tuyến
+
+Bạn có thể kiểm tra hầu hết các mô hình của chúng tôi trực tiếp trên trang của chúng từ [model hub](https://huggingface.co/models). Chúng tôi cũng cung cấp [dịch vụ lưu trữ mô hình riêng tư, phiên bản và API suy luận](https://huggingface.co/pricing) cho các mô hình công khai và riêng tư.
+
+Dưới đây là một số ví dụ:
+
+Trong Xử lý Ngôn ngữ Tự nhiên:
+- [Hoàn thành từ vụng về từ với BERT](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [Nhận dạng thực thể đặt tên với Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [Tạo văn bản tự nhiên với Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+- [Suy luận Ngôn ngữ Tự nhiên với RoBERTa](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [Tóm tắt văn bản với BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [Trả lời câu hỏi với DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [Dịch văn bản với T5](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+Trong Thị giác Máy tính:
+- [Phân loại hình ảnh với ViT](https://huggingface.co/google/vit-base-patch16-224)
+- [Phát hiện đối tượng với DETR](https://huggingface.co/facebook/detr-resnet-50)
+- [Phân đoạn ngữ nghĩa với SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
+- [Phân đoạn toàn diện với Mask2Former](https://huggingface.co/facebook/mask2former-swin-large-coco-panoptic)
+- [Ước lượng độ sâu với Depth Anything](https://huggingface.co/docs/transformers/main/model_doc/depth_anything)
+- [Phân loại video với VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)
+- [Phân đoạn toàn cầu với OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_dinat_large)
+
+Trong âm thanh:
+- [Nhận dạng giọng nói tự động với Whisper](https://huggingface.co/openai/whisper-large-v3)
+- [Phát hiện từ khóa với Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
+- [Phân loại âm thanh với Audio Spectrogram Transformer](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
+
+Trong các nhiệm vụ đa phương thức:
+- [Trả lời câu hỏi về bảng với TAPAS](https://huggingface.co/google/tapas-base-finetuned-wtq)
+- [Trả lời câu hỏi hình ảnh với ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
+- [Mô tả hình ảnh với LLaVa](https://huggingface.co/llava-hf/llava-1.5-7b-hf)
+- [Phân loại hình ảnh không cần nhãn với SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384)
+- [Trả lời câu hỏi văn bản tài liệu với LayoutLM](https://huggingface.co/impira/layoutlm-document-qa)
+- [Phân loại video không cần nhãn với X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)
+- [Phát hiện đối tượng không cần nhãn với OWLv2](https://huggingface.co/docs/transformers/en/model_doc/owlv2)
+- [Phân đoạn hình ảnh không cần nhãn với CLIPSeg](https://huggingface.co/docs/transformers/model_doc/clipseg)
+- [Tạo mặt nạ tự động với SAM](https://huggingface.co/docs/transformers/model_doc/sam)
+
+
+## 100 dự án sử dụng Transformers
+
+Transformers không chỉ là một bộ công cụ để sử dụng các mô hình được huấn luyện trước: đó là một cộng đồng các dự án xây dựng xung quanh nó và Hugging Face Hub. Chúng tôi muốn Transformers giúp các nhà phát triển, nhà nghiên cứu, sinh viên, giáo sư, kỹ sư và bất kỳ ai khác xây dựng những dự án mơ ước của họ.
+
+Để kỷ niệm 100.000 sao của transformers, chúng tôi đã quyết định tập trung vào cộng đồng và tạo ra trang [awesome-transformers](./awesome-transformers.md) liệt kê 100 dự án tuyệt vời được xây dựng xung quanh transformers.
+
+Nếu bạn sở hữu hoặc sử dụng một dự án mà bạn tin rằng nên được thêm vào danh sách, vui lòng mở một PR để thêm nó!
+
+## Nếu bạn đang tìm kiếm hỗ trợ tùy chỉnh từ đội ngũ Hugging Face
+
+
+
++ +## Hành trình nhanh + +Để ngay lập tức sử dụng một mô hình trên một đầu vào cụ thể (văn bản, hình ảnh, âm thanh, ...), chúng tôi cung cấp API `pipeline`. Pipelines nhóm một mô hình được huấn luyện trước với quá trình tiền xử lý đã được sử dụng trong quá trình huấn luyện của mô hình đó. Dưới đây là cách sử dụng nhanh một pipeline để phân loại văn bản tích cực so với tiêu cực: + +```python +>>> from transformers import pipeline + +# Cấp phát một pipeline cho phân tích cảm xúc +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +Dòng code thứ hai tải xuống và lưu trữ bộ mô hình được huấn luyện được sử dụng bởi pipeline, trong khi dòng thứ ba đánh giá nó trên văn bản đã cho. Ở đây, câu trả lời là "tích cực" với độ tin cậy là 99,97%. + +Nhiều nhiệm vụ có sẵn một `pipeline` được huấn luyện trước, trong NLP nhưng cũng trong thị giác máy tính và giọng nói. Ví dụ, chúng ta có thể dễ dàng trích xuất các đối tượng được phát hiện trong một hình ảnh: + +``` python +>>> import requests +>>> from PIL import Image +>>> from transformers import pipeline + +# Tải xuống một hình ảnh với những con mèo dễ thương +>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" +>>> image_data = requests.get(url, stream=True).raw +>>> image = Image.open(image_data) + +# Cấp phát một pipeline cho phát hiện đối tượng +>>> object_detector = pipeline('object-detection') +>>> object_detector(image) +[{'score': 0.9982201457023621, + 'label': 'remote', + 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}}, + {'score': 0.9960021376609802, + 'label': 'remote', + 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}}, + {'score': 0.9954745173454285, + 'label': 'couch', + 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}}, + {'score': 0.9988006353378296, + 'label': 'cat', + 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}}, + {'score': 0.9986783862113953, + 'label': 'cat', + 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}] +``` + +Ở đây, chúng ta nhận được một danh sách các đối tượng được phát hiện trong hình ảnh, với một hộp bao quanh đối tượng và một điểm đánh giá độ tin cậy. Đây là hình ảnh gốc ở bên trái, với các dự đoán hiển thị ở bên phải: + +
+
+
+
+
+Bạn có thể tìm hiểu thêm về các nhiệm vụ được hỗ trợ bởi API `pipeline` trong [hướng dẫn này](https://huggingface.co/docs/transformers/task_summary).
+
+Ngoài `pipeline`, để tải xuống và sử dụng bất kỳ mô hình được huấn luyện trước nào cho nhiệm vụ cụ thể của bạn, chỉ cần ba dòng code. Đây là phiên bản PyTorch:
+```python
+>>> from transformers import AutoTokenizer, AutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+
+Và đây là mã tương đương cho TensorFlow:
+```python
+>>> from transformers import AutoTokenizer, TFAutoModel
+
+>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
+>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased")
+
+>>> inputs = tokenizer("Hello world!", return_tensors="tf")
+>>> outputs = model(**inputs)
+```
+
+Tokenizer là thành phần chịu trách nhiệm cho việc tiền xử lý mà mô hình được huấn luyện trước mong đợi và có thể được gọi trực tiếp trên một chuỗi đơn (như trong các ví dụ trên) hoặc một danh sách. Nó sẽ xuất ra một từ điển mà bạn có thể sử dụng trong mã phụ thuộc hoặc đơn giản là truyền trực tiếp cho mô hình của bạn bằng cách sử dụng toán tử ** để giải nén đối số.
+
+Chính mô hình là một [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) thông thường hoặc một [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (tùy thuộc vào backend của bạn) mà bạn có thể sử dụng như bình thường. [Hướng dẫn này](https://huggingface.co/docs/transformers/training) giải thích cách tích hợp một mô hình như vậy vào một vòng lặp huấn luyện cổ điển PyTorch hoặc TensorFlow, hoặc cách sử dụng API `Trainer` của chúng tôi để tinh chỉnh nhanh chóng trên một bộ dữ liệu mới.
+
+## Tại sao tôi nên sử dụng transformers?
+
+1. Các mô hình tiên tiến dễ sử dụng:
+ - Hiệu suất cao trong việc hiểu và tạo ra ngôn ngữ tự nhiên, thị giác máy tính và âm thanh.
+ - Ngưỡng vào thấp cho giảng viên và người thực hành.
+ - Ít trừu tượng dành cho người dùng với chỉ ba lớp học.
+ - Một API thống nhất để sử dụng tất cả các mô hình được huấn luyện trước của chúng tôi.
+
+2. Giảm chi phí tính toán, làm giảm lượng khí thải carbon:
+ - Các nhà nghiên cứu có thể chia sẻ các mô hình đã được huấn luyện thay vì luôn luôn huấn luyện lại.
+ - Người thực hành có thể giảm thời gian tính toán và chi phí sản xuất.
+ - Hàng chục kiến trúc với hơn 400.000 mô hình được huấn luyện trước trên tất cả các phương pháp.
+
+3. Lựa chọn framework phù hợp cho mọi giai đoạn của mô hình:
+ - Huấn luyện các mô hình tiên tiến chỉ trong 3 dòng code.
+ - Di chuyển một mô hình duy nhất giữa các framework TF2.0/PyTorch/JAX theo ý muốn.
+ - Dễ dàng chọn framework phù hợp cho huấn luyện, đánh giá và sản xuất.
+
+4. Dễ dàng tùy chỉnh một mô hình hoặc một ví dụ theo nhu cầu của bạn:
+ - Chúng tôi cung cấp các ví dụ cho mỗi kiến trúc để tái tạo kết quả được công bố bởi các tác giả gốc.
+ - Các thành phần nội tại của mô hình được tiết lộ một cách nhất quán nhất có thể.
+ - Các tệp mô hình có thể được sử dụng độc lập với thư viện để thực hiện các thử nghiệm nhanh chóng.
+
+## Tại sao tôi không nên sử dụng transformers?
+
+- Thư viện này không phải là một bộ công cụ modul cho các khối xây dựng mạng neural. Mã trong các tệp mô hình không được tái cấu trúc với các trừu tượng bổ sung một cách cố ý, để các nhà nghiên cứu có thể lặp nhanh trên từng mô hình mà không cần đào sâu vào các trừu tượng/tệp bổ sung.
+- API huấn luyện không được thiết kế để hoạt động trên bất kỳ mô hình nào, mà được tối ưu hóa để hoạt động với các mô hình được cung cấp bởi thư viện. Đối với vòng lặp học máy chung, bạn nên sử dụng một thư viện khác (có thể là [Accelerate](https://huggingface.co/docs/accelerate)).
+- Mặc dù chúng tôi cố gắng trình bày càng nhiều trường hợp sử dụng càng tốt, nhưng các tập lệnh trong thư mục [examples](https://github.com/huggingface/transformers/tree/main/examples) chỉ là ví dụ. Dự kiến rằng chúng sẽ không hoạt động ngay tức khắc trên vấn đề cụ thể của bạn và bạn sẽ phải thay đổi một số dòng mã để thích nghi với nhu cầu của bạn.
+
+## Cài đặt
+
+### Sử dụng pip
+
+Thư viện này được kiểm tra trên Python 3.9+, Flax 0.4.1+, PyTorch 2.1+ và TensorFlow 2.6+.
+
+Bạn nên cài đặt 🤗 Transformers trong một [môi trường ảo Python](https://docs.python.org/3/library/venv.html). Nếu bạn chưa quen với môi trường ảo Python, hãy xem [hướng dẫn sử dụng](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
+
+Trước tiên, tạo một môi trường ảo với phiên bản Python bạn sẽ sử dụng và kích hoạt nó.
+
+Sau đó, bạn sẽ cần cài đặt ít nhất một trong số các framework Flax, PyTorch hoặc TensorFlow.
+Vui lòng tham khảo [trang cài đặt TensorFlow](https://www.tensorflow.org/install/), [trang cài đặt PyTorch](https://pytorch.org/get-started/locally/#start-locally) và/hoặc [Flax](https://github.com/google/flax#quick-install) và [Jax](https://github.com/google/jax#installation) để biết lệnh cài đặt cụ thể cho nền tảng của bạn.
+
+Khi đã cài đặt một trong các backend đó, 🤗 Transformers có thể được cài đặt bằng pip như sau:
+
+```bash
+pip install transformers
+```
+
+Nếu bạn muốn thực hiện các ví dụ hoặc cần phiên bản mới nhất của mã và không thể chờ đợi cho một phiên bản mới, bạn phải [cài đặt thư viện từ nguồn](https://huggingface.co/docs/transformers/installation#installing-from-source).
+
+### Với conda
+
+🤗 Transformers có thể được cài đặt bằng conda như sau:
+
+```shell script
+conda install conda-forge::transformers
+```
+
+> **_GHI CHÚ:_** Cài đặt `transformers` từ kênh `huggingface` đã bị lỗi thời.
+
+Hãy làm theo trang cài đặt của Flax, PyTorch hoặc TensorFlow để xem cách cài đặt chúng bằng conda.
+
+> **_GHI CHÚ:_** Trên Windows, bạn có thể được yêu cầu kích hoạt Chế độ phát triển để tận dụng việc lưu cache. Nếu điều này không phải là một lựa chọn cho bạn, hãy cho chúng tôi biết trong [vấn đề này](https://github.com/huggingface/huggingface_hub/issues/1062).
+
+## Kiến trúc mô hình
+
+**[Tất cả các điểm kiểm tra mô hình](https://huggingface.co/models)** được cung cấp bởi 🤗 Transformers được tích hợp một cách mượt mà từ trung tâm mô hình huggingface.co [model hub](https://huggingface.co/models), nơi chúng được tải lên trực tiếp bởi [người dùng](https://huggingface.co/users) và [tổ chức](https://huggingface.co/organizations).
+
+Số lượng điểm kiểm tra hiện tại: 
+
+🤗 Transformers hiện đang cung cấp các kiến trúc sau đây: xem [ở đây](https://huggingface.co/docs/transformers/model_summary) để có một tóm tắt tổng quan về mỗi kiến trúc.
+
+Để kiểm tra xem mỗi mô hình có một phiên bản thực hiện trong Flax, PyTorch hoặc TensorFlow, hoặc có một tokenizer liên quan được hỗ trợ bởi thư viện 🤗 Tokenizers, vui lòng tham khảo [bảng này](https://huggingface.co/docs/transformers/index#supported-frameworks).
+
+Những phiên bản này đã được kiểm tra trên một số tập dữ liệu (xem các tập lệnh ví dụ) và nên tương đương với hiệu suất của các phiên bản gốc. Bạn có thể tìm thấy thêm thông tin về hiệu suất trong phần Ví dụ của [tài liệu](https://github.com/huggingface/transformers/tree/main/examples).
+
+
+## Tìm hiểu thêm
+
+| Phần | Mô tả |
+|-|-|
+| [Tài liệu](https://huggingface.co/docs/transformers/) | Toàn bộ tài liệu API và hướng dẫn |
+| [Tóm tắt nhiệm vụ](https://huggingface.co/docs/transformers/task_summary) | Các nhiệm vụ được hỗ trợ bởi 🤗 Transformers |
+| [Hướng dẫn tiền xử lý](https://huggingface.co/docs/transformers/preprocessing) | Sử dụng lớp `Tokenizer` để chuẩn bị dữ liệu cho các mô hình |
+| [Huấn luyện và điều chỉnh](https://huggingface.co/docs/transformers/training) | Sử dụng các mô hình được cung cấp bởi 🤗 Transformers trong vòng lặp huấn luyện PyTorch/TensorFlow và API `Trainer` |
+| [Hướng dẫn nhanh: Điều chỉnh/sử dụng các kịch bản](https://github.com/huggingface/transformers/tree/main/examples) | Các kịch bản ví dụ để điều chỉnh mô hình trên nhiều nhiệm vụ khác nhau |
+| [Chia sẻ và tải lên mô hình](https://huggingface.co/docs/transformers/model_sharing) | Tải lên và chia sẻ các mô hình đã điều chỉnh của bạn với cộng đồng |
+
+## Trích dẫn
+
+Bây giờ chúng ta có một [bài báo](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) mà bạn có thể trích dẫn cho thư viện 🤗 Transformers:
+```bibtex
+@inproceedings{wolf-etal-2020-transformers,
+ title = "Transformers: State-of-the-Art Natural Language Processing",
+ author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
+ booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
+ month = oct,
+ year = "2020",
+ address = "Online",
+ publisher = "Association for Computational Linguistics",
+ url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
+ pages = "38--45"
+}
+```
diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_zh-hans.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_zh-hans.md
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+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
为 Jax、PyTorch 和 TensorFlow 打造的先进的自然语言处理
+ + +
+
+
+
+🤗 Transformers 提供了数以千计的预训练模型，支持 100 多种语言的文本分类、信息抽取、问答、摘要、翻译、文本生成。它的宗旨是让最先进的 NLP 技术人人易用。
+
+🤗 Transformers 提供了便于快速下载和使用的API，让你可以把预训练模型用在给定文本、在你的数据集上微调然后通过 [model hub](https://huggingface.co/models) 与社区共享。同时，每个定义的 Python 模块都是完全独立的，便于修改和快速进行研究实验。
+
+🤗 Transformers 支持三个最热门的深度学习库： [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) 以及 [TensorFlow](https://www.tensorflow.org/) — 并与之无缝整合。你可以直接使用一个框架训练你的模型然后用另一个加载和推理。
+
+## 在线演示
+
+你可以直接在模型页面上测试大多数 [model hub](https://huggingface.co/models) 上的模型。 我们也提供了 [私有模型托管、模型版本管理以及推理API](https://huggingface.co/pricing)。
+
+这里是一些例子：
+- [用 BERT 做掩码填词](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [用 Electra 做命名实体识别](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [用 GPT-2 做文本生成](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
+- [用 RoBERTa 做自然语言推理](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [用 BART 做文本摘要](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [用 DistilBERT 做问答](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [用 T5 做翻译](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+**[Write With Transformer](https://transformer.huggingface.co)**，由抱抱脸团队打造，是一个文本生成的官方 demo。
+
+## 如果你在寻找由抱抱脸团队提供的定制化支持服务
+
+
+
++ +## 快速上手 + +我们为快速使用模型提供了 `pipeline` （流水线）API。流水线聚合了预训练模型和对应的文本预处理。下面是一个快速使用流水线去判断正负面情绪的例子： + +```python +>>> from transformers import pipeline + +# 使用情绪分析流水线 +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +第二行代码下载并缓存了流水线使用的预训练模型，而第三行代码则在给定的文本上进行了评估。这里的答案“正面” (positive) 具有 99 的置信度。 + +许多的 NLP 任务都有开箱即用的预训练流水线。比如说，我们可以轻松的从给定文本中抽取问题答案： + +``` python +>>> from transformers import pipeline + +# 使用问答流水线 +>>> question_answerer = pipeline('question-answering') +>>> question_answerer({ +... 'question': 'What is the name of the repository ?', +... 'context': 'Pipeline has been included in the huggingface/transformers repository' +... }) +{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'} + +``` + +除了给出答案，预训练模型还给出了对应的置信度分数、答案在词符化 (tokenized) 后的文本中开始和结束的位置。你可以从[这个教程](https://huggingface.co/docs/transformers/task_summary)了解更多流水线API支持的任务。 + +要在你的任务上下载和使用任意预训练模型也很简单，只需三行代码。这里是 PyTorch 版的示例： +```python +>>> from transformers import AutoTokenizer, AutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="pt") +>>> outputs = model(**inputs) +``` +这里是等效的 TensorFlow 代码： +```python +>>> from transformers import AutoTokenizer, TFAutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="tf") +>>> outputs = model(**inputs) +``` + +词符化器 (tokenizer) 为所有的预训练模型提供了预处理，并可以直接对单个字符串进行调用（比如上面的例子）或对列表 (list) 调用。它会输出一个你可以在下游代码里使用或直接通过 `**` 解包表达式传给模型的词典 (dict)。 + +模型本身是一个常规的 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) 或 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)（取决于你的后端），可以常规方式使用。 [这个教程](https://huggingface.co/transformers/training.html)解释了如何将这样的模型整合到经典的 PyTorch 或 TensorFlow 训练循环中，或是如何使用我们的 `Trainer` 训练器）API 来在一个新的数据集上快速微调。 + +## 为什么要用 transformers？ + +1. 便于使用的先进模型： + - NLU 和 NLG 上表现优越 + - 对教学和实践友好且低门槛 + - 高级抽象，只需了解三个类 + - 对所有模型统一的API + +1. 更低计算开销，更少的碳排放： + - 研究人员可以分享已训练的模型而非每次从头开始训练 + - 工程师可以减少计算用时和生产环境开销 + - 数十种模型架构、两千多个预训练模型、100多种语言支持 + +1. 对于模型生命周期的每一个部分都面面俱到： + - 训练先进的模型，只需 3 行代码 + - 模型在不同深度学习框架间任意转移，随你心意 + - 为训练、评估和生产选择最适合的框架，衔接无缝 + +1. 为你的需求轻松定制专属模型和用例： + - 我们为每种模型架构提供了多个用例来复现原论文结果 + - 模型内部结构保持透明一致 + - 模型文件可单独使用，方便魔改和快速实验 + +## 什么情况下我不该用 transformers？ + +- 本库并不是模块化的神经网络工具箱。模型文件中的代码特意呈若璞玉，未经额外抽象封装，以便研究人员快速迭代魔改而不致溺于抽象和文件跳转之中。 +- `Trainer` API 并非兼容任何模型，只为本库之模型优化。若是在寻找适用于通用机器学习的训练循环实现，请另觅他库。 +- 尽管我们已尽力而为，[examples 目录](https://github.com/huggingface/transformers/tree/main/examples)中的脚本也仅为用例而已。对于你的特定问题，它们并不一定开箱即用，可能需要改几行代码以适之。 + +## 安装 + +### 使用 pip + +这个仓库已在 Python 3.9+、Flax 0.4.1+、PyTorch 2.1+ 和 TensorFlow 2.6+ 下经过测试。 + +你可以在[虚拟环境](https://docs.python.org/3/library/venv.html)中安装 🤗 Transformers。如果你还不熟悉 Python 的虚拟环境，请阅此[用户说明](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)。 + +首先，用你打算使用的版本的 Python 创建一个虚拟环境并激活。 + +然后，你需要安装 Flax、PyTorch 或 TensorFlow 其中之一。关于在你使用的平台上安装这些框架，请参阅 [TensorFlow 安装页](https://www.tensorflow.org/install/), [PyTorch 安装页](https://pytorch.org/get-started/locally/#start-locally) 或 [Flax 安装页](https://github.com/google/flax#quick-install)。 + +当这些后端之一安装成功后， 🤗 Transformers 可依此安装： + +```bash +pip install transformers +``` + +如果你想要试试用例或者想在正式发布前使用最新的开发中代码，你得[从源代码安装](https://huggingface.co/docs/transformers/installation#installing-from-source)。 + +### 使用 conda + +🤗 Transformers 可以通过 conda 依此安装： + +```shell script +conda install conda-forge::transformers +``` + +> **_笔记:_** 从 `huggingface` 渠道安装 `transformers` 已被废弃。 + +要通过 conda 安装 Flax、PyTorch 或 TensorFlow 其中之一，请参阅它们各自安装页的说明。 + +## 模型架构 + +🤗 Transformers 支持的[**所有的模型检查点**](https://huggingface.co/models)由[用户](https://huggingface.co/users)和[组织](https://huggingface.co/organizations)上传，均与 huggingface.co [model hub](https://huggingface.co) 无缝整合。 + +目前的检查点数量：  + +🤗 Transformers 目前支持如下的架构: 模型概述请阅[这里](https://huggingface.co/docs/transformers/model_summary). + +要检查某个模型是否已有 Flax、PyTorch 或 TensorFlow 的实现，或其是否在 🤗 Tokenizers 库中有对应词符化器（tokenizer），敬请参阅[此表](https://huggingface.co/docs/transformers/index#supported-frameworks)。 + +这些实现均已于多个数据集测试（请参看用例脚本）并应于原版实现表现相当。你可以在用例文档的[此节](https://huggingface.co/docs/transformers/examples)中了解表现的细节。 + + +## 了解更多 + +| 章节 | 描述 | +|-|-| +| [文档](https://huggingface.co/docs/transformers/) | 完整的 API 文档和教程 | +| [任务总结](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers 支持的任务 | +| [预处理教程](https://huggingface.co/docs/transformers/preprocessing) | 使用 `Tokenizer` 来为模型准备数据 | +| [训练和微调](https://huggingface.co/docs/transformers/training) | 在 PyTorch/TensorFlow 的训练循环或 `Trainer` API 中使用 🤗 Transformers 提供的模型 | +| [快速上手：微调和用例脚本](https://github.com/huggingface/transformers/tree/main/examples) | 为各种任务提供的用例脚本 | +| [模型分享和上传](https://huggingface.co/docs/transformers/model_sharing) | 和社区上传和分享你微调的模型 | +| [迁移](https://huggingface.co/docs/transformers/migration) | 从 `pytorch-transformers` 或 `pytorch-pretrained-bert` 迁移到 🤗 Transformers | + +## 引用 + +我们已将此库的[论文](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)正式发表，如果你使用了 🤗 Transformers 库，请引用: +```bibtex +@inproceedings{wolf-etal-2020-transformers, + title = "Transformers: State-of-the-Art Natural Language Processing", + author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = oct, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", + pages = "38--45" +} +``` diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_zh-hant.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_zh-hant.md new file mode 100644 index 0000000000000000000000000000000000000000..da6ed40910ea6c791d8824061518ab38e9ac3e82 --- /dev/null +++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/i18n/README_zh-hant.md @@ -0,0 +1,286 @@ + + + + +
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+ English | + 简体中文 | + 繁體中文 | + 한국어 | + Español | + 日本語 | + हिन्दी | + Русский | + Рortuguês | + తెలుగు | + Français | + Deutsch | + Tiếng Việt | + العربية | + اردو | +
+ + ++
為 Jax、PyTorch 以及 TensorFlow 打造的先進自然語言處理函式庫
+ + +
+
+
+
+🤗 Transformers 提供了數以千計的預訓練模型，支援 100 多種語言的文本分類、資訊擷取、問答、摘要、翻譯、文本生成。它的宗旨是讓最先進的 NLP 技術人人易用。
+
+🤗 Transformers 提供了便於快速下載和使用的API，讓你可以將預訓練模型用在給定文本、在你的資料集上微調然後經由 [model hub](https://huggingface.co/models) 與社群共享。同時，每個定義的 Python 模組架構均完全獨立，方便修改和快速研究實驗。
+
+🤗 Transformers 支援三個最熱門的深度學習函式庫： [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) 以及 [TensorFlow](https://www.tensorflow.org/) — 並與之完美整合。你可以直接使用其中一個框架訓練你的模型，然後用另一個載入和推論。
+
+## 線上Demo
+
+你可以直接在 [model hub](https://huggingface.co/models) 上測試大多數的模型。我們也提供了 [私有模型託管、模型版本管理以及推論API](https://huggingface.co/pricing)。
+
+這裡是一些範例：
+- [用 BERT 做遮蓋填詞](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
+- [用 Electra 做專有名詞辨識](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
+- [用 GPT-2 做文本生成](https://huggingface.co/openai-community/gpt2?text=A+long+time+ago%2C+)
+- [用 RoBERTa 做自然語言推論](https://huggingface.co/FacebookAI/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
+- [用 BART 做文本摘要](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
+- [用 DistilBERT 做問答](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
+- [用 T5 做翻譯](https://huggingface.co/google-t5/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
+
+**[Write With Transformer](https://transformer.huggingface.co)**，由 Hugging Face 團隊所打造，是一個文本生成的官方 demo。
+
+## 如果你在尋找由 Hugging Face 團隊所提供的客製化支援服務
+
+
+
++ +## 快速上手 + +我們為快速使用模型提供了 `pipeline` API。 Pipeline 包含了預訓練模型和對應的文本預處理。下面是一個快速使用 pipeline 去判斷正負面情緒的例子： + +```python +>>> from transformers import pipeline + +# 使用情緒分析 pipeline +>>> classifier = pipeline('sentiment-analysis') +>>> classifier('We are very happy to introduce pipeline to the transformers repository.') +[{'label': 'POSITIVE', 'score': 0.9996980428695679}] +``` + +第二行程式碼下載並快取 pipeline 使用的預訓練模型，而第三行程式碼則在給定的文本上進行了評估。這裡的答案“正面” (positive) 具有 99.97% 的信賴度。 + +許多的 NLP 任務都有隨選即用的預訓練 `pipeline`。例如，我們可以輕鬆地從給定文本中擷取問題答案： + +``` python +>>> from transformers import pipeline + +# 使用問答 pipeline +>>> question_answerer = pipeline('question-answering') +>>> question_answerer({ +... 'question': 'What is the name of the repository ?', +... 'context': 'Pipeline has been included in the huggingface/transformers repository' +... }) +{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'} + +``` + +除了提供問題解答，預訓練模型還提供了對應的信賴度分數以及解答在 tokenized 後的文本中開始和結束的位置。你可以從[這個教學](https://huggingface.co/docs/transformers/task_summary)了解更多 `pipeline` API支援的任務。 + +要在你的任務中下載和使用任何預訓練模型很簡單，只需三行程式碼。這裡是 PyTorch 版的範例： +```python +>>> from transformers import AutoTokenizer, AutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = AutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="pt") +>>> outputs = model(**inputs) +``` +這裡是對應的 TensorFlow 程式碼： +```python +>>> from transformers import AutoTokenizer, TFAutoModel + +>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") +>>> model = TFAutoModel.from_pretrained("google-bert/bert-base-uncased") + +>>> inputs = tokenizer("Hello world!", return_tensors="tf") +>>> outputs = model(**inputs) +``` + +Tokenizer 為所有的預訓練模型提供了預處理，並可以直接轉換單一字串（比如上面的例子）或串列 (list)。它會輸出一個的字典 (dict) 讓你可以在下游程式碼裡使用或直接藉由 `**` 運算式傳給模型。 + +模型本身是一個常規的 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) 或 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)（取決於你的後端），可依常規方式使用。 [這個教學](https://huggingface.co/transformers/training.html)解釋了如何將這樣的模型整合到一般的 PyTorch 或 TensorFlow 訓練迴圈中，或是如何使用我們的 `Trainer` API 在一個新的資料集上快速進行微調。 + +## 為什麼要用 transformers？ + +1. 便於使用的先進模型： + - NLU 和 NLG 上性能卓越 + - 對教學和實作友好且低門檻 + - 高度抽象，使用者只須學習 3 個類別 + - 對所有模型使用的制式化API + +1. 更低的運算成本，更少的碳排放： + - 研究人員可以分享已訓練的模型而非每次從頭開始訓練 + - 工程師可以減少計算時間以及生產成本 + - 數十種模型架構、兩千多個預訓練模型、100多種語言支援 + +1. 對於模型生命週期的每一個部分都面面俱到： + - 訓練先進的模型，只需 3 行程式碼 + - 模型可以在不同深度學習框架之間任意轉換 + - 為訓練、評估和生產選擇最適合的框架，並完美銜接 + +1. 為你的需求輕鬆客製化專屬模型和範例： + - 我們為每種模型架構提供了多個範例來重現原論文結果 + - 一致的模型內部架構 + - 模型檔案可單獨使用，便於修改和快速實驗 + +## 什麼情況下我不該用 transformers？ + +- 本函式庫並不是模組化的神經網絡工具箱。模型文件中的程式碼並未做額外的抽象封裝，以便研究人員快速地翻閱及修改程式碼，而不會深陷複雜的類別包裝之中。 +- `Trainer` API 並非相容任何模型，它只為本函式庫中的模型最佳化。對於一般的機器學習用途，請使用其他函式庫。 +- 儘管我們已盡力而為，[examples 目錄](https://github.com/huggingface/transformers/tree/main/examples)中的腳本也僅為範例而已。對於特定問題，它們並不一定隨選即用，可能需要修改幾行程式碼以符合需求。 + +## 安裝 + +### 使用 pip + +這個 Repository 已在 Python 3.9+、Flax 0.4.1+、PyTorch 2.1+ 和 TensorFlow 2.6+ 下經過測試。 + +你可以在[虛擬環境](https://docs.python.org/3/library/venv.html)中安裝 🤗 Transformers。如果你還不熟悉 Python 的虛擬環境，請閱此[使用者指引](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)。 + +首先，用你打算使用的版本的 Python 創建一個虛擬環境並進入。 + +然後，你需要安裝 Flax、PyTorch 或 TensorFlow 其中之一。對於該如何在你使用的平台上安裝這些框架，請參閱 [TensorFlow 安裝頁面](https://www.tensorflow.org/install/), [PyTorch 安裝頁面](https://pytorch.org/get-started/locally/#start-locally) 或 [Flax 安裝頁面](https://github.com/google/flax#quick-install)。 + +當其中一個後端安裝成功後，🤗 Transformers 可依此安裝： + +```bash +pip install transformers +``` + +如果你想要試試範例或者想在正式發布前使用最新開發中的程式碼，你必須[從原始碼安裝](https://huggingface.co/docs/transformers/installation#installing-from-source)。 + +### 使用 conda + +🤗 Transformers 可以藉由 conda 依此安裝： + +```shell script +conda install conda-forge::transformers +``` + +> **_筆記:_** 從 `huggingface` 頻道安裝 `transformers` 已被淘汰。 + +要藉由 conda 安裝 Flax、PyTorch 或 TensorFlow 其中之一，請參閱它們各自安裝頁面的說明。 + +## 模型架構 + +**🤗 Transformers 支援的[所有的模型檢查點](https://huggingface.co/models)**，由[使用者](https://huggingface.co/users)和[組織](https://huggingface.co/organizations)上傳，均與 huggingface.co [model hub](https://huggingface.co) 完美結合。 + +目前的檢查點數量：  + +🤗 Transformers 目前支援以下的架構: 模型概覽請參閱[這裡](https://huggingface.co/docs/transformers/model_summary). + +要檢查某個模型是否已有 Flax、PyTorch 或 TensorFlow 的實作，或其是否在🤗 Tokenizers 函式庫中有對應的 tokenizer，敬請參閱[此表](https://huggingface.co/docs/transformers/index#supported-frameworks)。 + +這些實作均已於多個資料集測試（請參閱範例腳本）並應與原版實作表現相當。你可以在範例文件的[此節](https://huggingface.co/docs/transformers/examples)中了解實作的細節。 + + +## 了解更多 + +| 章節 | 描述 | +|-|-| +| [文件](https://huggingface.co/transformers/) | 完整的 API 文件和教學 | +| [任務概覽](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers 支援的任務 | +| [預處理教學](https://huggingface.co/docs/transformers/preprocessing) | 使用 `Tokenizer` 來為模型準備資料 | +| [訓練和微調](https://huggingface.co/docs/transformers/training) | 使用 PyTorch/TensorFlow 的內建的訓練方式或於 `Trainer` API 中使用 🤗 Transformers 提供的模型 | +| [快速上手：微調和範例腳本](https://github.com/huggingface/transformers/tree/main/examples) | 為各種任務提供的範例腳本 | +| [模型分享和上傳](https://huggingface.co/docs/transformers/model_sharing) | 上傳並與社群分享你微調的模型 | +| [遷移](https://huggingface.co/docs/transformers/migration) | 從 `pytorch-transformers` 或 `pytorch-pretrained-bert` 遷移到 🤗 Transformers | + +## 引用 + +我們已將此函式庫的[論文](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)正式發表。如果你使用了 🤗 Transformers 函式庫，可以引用： +```bibtex +@inproceedings{wolf-etal-2020-transformers, + title = "Transformers: State-of-the-Art Natural Language Processing", + author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush", + booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations", + month = oct, + year = "2020", + address = "Online", + publisher = "Association for Computational Linguistics", + url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6", + pages = "38--45" +} +``` diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/notebooks/README.md b/tasks/tasksmith-5cbfd9f64dbc/tests/source/notebooks/README.md new file mode 100644 index 0000000000000000000000000000000000000000..99ff598931cc4cb690e579b7b87d9532492dcff3 --- /dev/null +++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/notebooks/README.md @@ -0,0 +1,152 @@ + + +# 🤗 Transformers Notebooks + +You can find here a list of the official notebooks provided by Hugging Face. + +Also, we would like to list here interesting content created by the community. +If you wrote some notebook(s) leveraging 🤗 Transformers and would like to be listed here, please open a +Pull Request so it can be included under the Community notebooks. + + +## Hugging Face's notebooks 🤗 + +### Documentation notebooks + +You can open any page of the documentation as a notebook in Colab (there is a button directly on said pages) but they are also listed here if you need them: + +| Notebook | Description | | | +|:----------|:-------------|:-------------|------:| +| [Quicktour of the library](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/quicktour.ipynb) | A presentation of the various APIs in Transformers |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/quicktour.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/en/transformers_doc/quicktour.ipynb)| +| [Summary of the tasks](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/task_summary.ipynb) | How to run the models of the Transformers library task by task |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/task_summary.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/task_summary.ipynb)| +| [Preprocessing data](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/preprocessing.ipynb) | How to use a tokenizer to preprocess your data |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/preprocessing.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/preprocessing.ipynb)| +| [Fine-tuning a pretrained model](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/training.ipynb) | How to use the Trainer to fine-tune a pretrained model |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/training.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/training.ipynb)| +| [Summary of the tokenizers](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/tokenizer_summary.ipynb) | The differences between the tokenizers algorithm |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/tokenizer_summary.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/tokenizer_summary.ipynb)| +| [Multilingual models](https://github.com/huggingface/notebooks/blob/main/transformers_doc/en/multilingual.ipynb) | How to use the multilingual models of the library |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/multilingual.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/multilingual.ipynb)| + + +### PyTorch Examples + +#### Natural Language Processing[[pytorch-nlp]] + +| Notebook | Description | | | +|:----------|:-------------|:-------------|------:| +| [Train your tokenizer](https://github.com/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb) | How to train and use your very own tokenizer |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)| +| [Train your language model](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch.ipynb) | How to easily start using transformers |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch.ipynb)| +| [How to fine-tune a model on text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)| +| [How to fine-tune a model on language modeling](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)| +| [How to fine-tune a model on token classification](https://github.com/huggingface/notebooks/blob/main/examples/token_classification.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb)| +| [How to fine-tune a model on question answering](https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb)| +| [How to fine-tune a model on multiple choice](https://github.com/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SWAG. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb)| +| [How to fine-tune a model on translation](https://github.com/huggingface/notebooks/blob/main/examples/translation.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on WMT. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/translation.ipynb)| +| [How to fine-tune a model on summarization](https://github.com/huggingface/notebooks/blob/main/examples/summarization.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on XSUM. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/summarization.ipynb)| +| [How to train a language model from scratch](https://github.com/huggingface/blog/blob/main/notebooks/01_how_to_train.ipynb)| Highlight all the steps to effectively train Transformer model on custom data | [](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/01_how_to_train.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/main/notebooks/01_how_to_train.ipynb)| +| [How to generate text](https://github.com/huggingface/blog/blob/main/notebooks/02_how_to_generate.ipynb)| How to use different decoding methods for language generation with transformers | [](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/02_how_to_generate.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/main/notebooks/02_how_to_generate.ipynb)| +| [How to generate text (with constraints)](https://github.com/huggingface/blog/blob/main/notebooks/53_constrained_beam_search.ipynb)| How to guide language generation with user-provided constraints | [](https://colab.research.google.com/github/huggingface/blog/blob/main/notebooks/53_constrained_beam_search.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/blog/blob/main/notebooks/53_constrained_beam_search.ipynb)| +| [Reformer](https://github.com/huggingface/blog/blob/main/notebooks/03_reformer.ipynb)| How Reformer pushes the limits of language modeling | [](https://colab.research.google.com/github/patrickvonplaten/blog/blob/main/notebooks/03_reformer.ipynb)| [](https://studiolab.sagemaker.aws/import/github/patrickvonplaten/blog/blob/main/notebooks/03_reformer.ipynb)| + +#### Computer Vision[[pytorch-cv]] + +| Notebook | Description | | | +|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------:| +| [How to fine-tune a model on image classification (Torchvision)](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb) | Show how to preprocess the data using Torchvision and fine-tune any pretrained Vision model on Image Classification | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb)| +| [How to fine-tune a model on image classification (Albumentations)](https://github.com/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb) | Show how to preprocess the data using Albumentations and fine-tune any pretrained Vision model on Image Classification | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb)| +| [How to fine-tune a model on image classification (Kornia)](https://github.com/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb) | Show how to preprocess the data using Kornia and fine-tune any pretrained Vision model on Image Classification | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb)| +| [How to perform zero-shot object detection with OWL-ViT](https://github.com/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb) | Show how to perform zero-shot object detection on images with text queries | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb)| +| [How to fine-tune an image captioning model](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb) | Show how to fine-tune BLIP for image captioning on a custom dataset | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb)| +| [How to build an image similarity system with Transformers](https://github.com/huggingface/notebooks/blob/main/examples/image_similarity.ipynb) | Show how to build an image similarity system | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_similarity.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_similarity.ipynb)| +| [How to fine-tune a SegFormer model on semantic segmentation](https://github.com/huggingface/notebooks/blob/main/examples/semantic_segmentation.ipynb) | Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/semantic_segmentation.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/semantic_segmentation.ipynb)| +| [How to fine-tune a VideoMAE model on video classification](https://github.com/huggingface/notebooks/blob/main/examples/video_classification.ipynb) | Show how to preprocess the data and fine-tune a pretrained VideoMAE model on Video Classification | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/video_classification.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/video_classification.ipynb)| + +#### Audio[[pytorch-audio]] + +| Notebook | Description | | | +|:----------|:-------------|:-------------|------:| +| [How to fine-tune a speech recognition model in English](https://github.com/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb)| Show how to preprocess the data and fine-tune a pretrained Speech model on TIMIT | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/speech_recognition.ipynb)| +| [How to fine-tune a speech recognition model in any language](https://github.com/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb)| Show how to preprocess the data and fine-tune a multi-lingually pretrained speech model on Common Voice | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/multi_lingual_speech_recognition.ipynb)| +| [How to fine-tune a model on audio classification](https://github.com/huggingface/notebooks/blob/main/examples/audio_classification.ipynb)| Show how to preprocess the data and fine-tune a pretrained Speech model on Keyword Spotting | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb)| + +#### Biological Sequences[[pytorch-bio]] + +| Notebook | Description | | | +|:----------|:----------------------------------------------------------------------------------------|:-------------|------:| +| [How to fine-tune a pre-trained protein model](https://github.com/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb) | See how to tokenize proteins and fine-tune a large pre-trained protein "language" model | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb) | +| [How to generate protein folds](https://github.com/huggingface/notebooks/blob/main/examples/protein_folding.ipynb) | See how to go from protein sequence to a full protein model and PDB file | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_folding.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/protein_folding.ipynb) | +| [How to fine-tune a Nucleotide Transformer model](https://github.com/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling.ipynb) | See how to tokenize DNA and fine-tune a large pre-trained DNA "language" model | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling.ipynb) | +| [Fine-tune a Nucleotide Transformer model with LoRA](https://github.com/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb) | Train even larger DNA models in a memory-efficient way | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb) | + + +#### Other modalities[[pytorch-other]] + +| Notebook | Description | | | +|:----------|:----------------------------------------------------------------------------------------|:-------------|------:| +| [Probabilistic Time Series Forecasting](https://github.com/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb) | See how to train Time Series Transformer on a custom dataset | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/time-series-transformers.ipynb) | + +#### Utility notebooks[[pytorch-utility]] + +| Notebook | Description | | | +|:----------|:-------------|:-------------|------:| +| [How to export model to ONNX](https://github.com/huggingface/notebooks/blob/main/examples/onnx-export.ipynb)| Highlight how to export and run inference workloads through ONNX | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/onnx-export.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/onnx-export.ipynb)| + +### TensorFlow Examples + +#### Natural Language Processing[[tensorflow-nlp]] + +| Notebook | Description | | | +|:----------|:-------------|:-------------|------:| +| [Train your tokenizer](https://github.com/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb) | How to train and use your very own tokenizer |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/tokenizer_training.ipynb)| +| [Train your language model](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch-tf.ipynb) | How to easily start using transformers |[](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling_from_scratch-tf.ipynb)| +| [How to fine-tune a model on text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on any GLUE task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb)| +| [How to fine-tune a model on language modeling](https://github.com/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)| +| [How to fine-tune a model on token classification](https://github.com/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS). | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)| +| [How to fine-tune a model on question answering](https://github.com/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SQUAD. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb)| +| [How to fine-tune a model on multiple choice](https://github.com/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on SWAG. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb)| +| [How to fine-tune a model on translation](https://github.com/huggingface/notebooks/blob/main/examples/translation-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on WMT. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb)| +| [How to fine-tune a model on summarization](https://github.com/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb)| Show how to preprocess the data and fine-tune a pretrained model on XSUM. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/summarization-tf.ipynb)| + +#### Computer Vision[[tensorflow-cv]] + +| Notebook | Description | | | +|:---------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:-------------|------:| +| [How to fine-tune a model on image classification](https://github.com/huggingface/notebooks/blob/main/examples/image_classification-tf.ipynb) | Show how to preprocess the data and fine-tune any pretrained Vision model on Image Classification | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/image_classification-tf.ipynb)| +| [How to fine-tune a SegFormer model on semantic segmentation](https://github.com/huggingface/notebooks/blob/main/examples/semantic_segmentation-tf.ipynb) | Show how to preprocess the data and fine-tune a pretrained SegFormer model on Semantic Segmentation | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/semantic_segmentation-tf.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/semantic_segmentation-tf.ipynb)| + +#### Biological Sequences[[tensorflow-bio]] + +| Notebook | Description | | | +|:----------|:-------------|:-------------|------:| +| [How to fine-tune a pre-trained protein model](https://github.com/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb) | See how to tokenize proteins and fine-tune a large pre-trained protein "language" model | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb) | + +#### Utility notebooks[[tensorflow-utility]] + +| Notebook | Description | | | +|:----------|:-------------|:-------------|------:| +| [How to train TF/Keras models on TPU](https://github.com/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) | See how to train at high speed on Google's TPU hardware | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) | [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) | + +### Optimum notebooks + +🤗 [Optimum](https://github.com/huggingface/optimum) is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardwares. + +| Notebook | Description | | | +|:----------|:-------------|:-------------|------:| +| [How to quantize a model with ONNX Runtime for text classification](https://github.com/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)| Show how to apply static and dynamic quantization on a model using [ONNX Runtime](https://github.com/microsoft/onnxruntime) for any GLUE task. | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification_quantization_ort.ipynb)| +| [How to fine-tune a model on text classification with ONNX Runtime](https://github.com/huggingface/notebooks/blob/main/examples/text_classification_ort.ipynb)| Show how to preprocess the data and fine-tune a model on any GLUE task using [ONNX Runtime](https://github.com/microsoft/onnxruntime). | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_ort.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/text_classification_ort.ipynb)| +| [How to fine-tune a model on summarization with ONNX Runtime](https://github.com/huggingface/notebooks/blob/main/examples/summarization_ort.ipynb)| Show how to preprocess the data and fine-tune a model on XSUM using [ONNX Runtime](https://github.com/microsoft/onnxruntime). | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/summarization_ort.ipynb)| [](https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/examples/summarization_ort.ipynb)| + +## Community notebooks: + +More notebooks developed by the community are available [here](https://hf.co/docs/transformers/community#community-notebooks). diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/pyproject.toml b/tasks/tasksmith-5cbfd9f64dbc/tests/source/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..b8f4676a2b46b8188619924e133d1ef240a7ab4a --- /dev/null +++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/pyproject.toml @@ -0,0 +1,65 @@ +[tool.coverage.run] +source = ["transformers"] +omit = [ + "*/convert_*", + "*/__main__.py" +] + +[tool.coverage.report] +exclude_lines = [ + "pragma: no cover", + "raise", + "except", + "register_parameter" +] + +[tool.ruff] +target-version = "py39" +line-length = 119 + +[tool.ruff.lint] +# Never enforce `E501` (line length violations). +# SIM300: Yoda condition detected +# SIM212: Checks for if expressions that check against a negated condition. +# SIM905: Consider using a list literal instead of `str.split` +ignore = ["C901", "E501", "E741", "F402", "F823", "SIM1", "SIM300", "SIM212", "SIM905"] +# RUF013: Checks for the use of implicit Optional +# in type annotations when the default parameter value is None. +select = ["C", "E", "F", "I", "W", "RUF013", "UP006", "PERF102", "PLC1802", "PLC0208","SIM"] +extend-safe-fixes = ["UP006"] + +# Ignore import violations in all `__init__.py` files. +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["E402", "F401", "F403", "F811"] +"src/transformers/file_utils.py" = ["F401"] +"src/transformers/utils/dummy_*.py" = ["F401"] + +[tool.ruff.lint.isort] +lines-after-imports = 2 +known-first-party = ["transformers"] + +[tool.ruff.format] +# Like Black, use double quotes for strings. +quote-style = "double" + +# Like Black, indent with spaces, rather than tabs. +indent-style = "space" + +# Like Black, respect magic trailing commas. +skip-magic-trailing-comma = false + +# Like Black, automatically detect the appropriate line ending. +line-ending = "auto" + +[tool.pytest.ini_options] +addopts = "--doctest-glob='**/*.md'" +doctest_optionflags="NUMBER NORMALIZE_WHITESPACE ELLIPSIS" +markers = [ + "flash_attn_3_test: marks tests related to flash attention 3 (deselect with '-m \"not flash_attn_3_test\"')", + "flash_attn_test: marks tests related to flash attention (deselect with '-m \"not flash_attn_test\"')", + "bitsandbytes: select (or deselect with `not`) bitsandbytes integration tests", + "generate: marks tests that use the GenerationTesterMixin" +] +log_cli = 1 +log_cli_level = "WARNING" +asyncio_default_fixture_loop_scope = "function" diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/scripts/check_tokenizers.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/scripts/check_tokenizers.py new file mode 100644 index 0000000000000000000000000000000000000000..a099d794c2b4b85f3866d17f2a917c0f3c849eb6 --- /dev/null +++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/scripts/check_tokenizers.py @@ -0,0 +1,173 @@ +from collections import Counter + +import datasets + +import transformers +from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS +from transformers.utils import logging +from transformers.tokenization_utils_base import PreTrainedTokenizerBase + + +logging.set_verbosity_info() + +TOKENIZER_CLASSES = { + name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS +} + +dataset = datasets.load_dataset("facebook/xnli", split="test+validation") # no-script + +total = 0 +perfect = 0 +imperfect = 0 +wrong = 0 + + +def check_diff(spm_diff: list[int], tok_diff: list[int], slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase) -> bool: + if spm_diff == list(reversed(tok_diff)): + # AAA -> AA+A vs A+AA case. + return True + elif len(spm_diff) == len(tok_diff) and fast.decode(spm_diff) == fast.decode(tok_diff): + # Second order OK + # Barrich -> Barr + ich vs Bar + rich + return True + spm_reencoded = slow.encode(slow.decode(spm_diff)) + tok_reencoded = fast.encode(fast.decode(spm_diff)) + if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded: + # Type 3 error. + # Snehagatha -> + # Sne, h, aga, th, a + # Sne, ha, gat, ha + # Encoding the wrong with sp does not even recover what spm gave us + # It fits tokenizer however... + return True + return False + + +def check_LTR_mark(line: str, idx: int, fast: PreTrainedTokenizerBase) -> bool: + enc = fast.encode_plus(line)[0] + offsets = enc.offsets + curr, prev = offsets[idx], offsets[idx - 1] + if curr is not None and line[curr[0] : curr[1]] == "\u200f": + return True + if prev is not None and line[prev[0] : prev[1]] == "\u200f": + return True + return False + + +def check_details(line: str, spm_ids: list[int], tok_ids: list[int], slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase) -> bool: + # Encoding can be the same with same result AAA -> A + AA vs AA + A + # We can check that we use at least exactly the same number of tokens. + for i, (spm_id, tok_id) in enumerate(zip(spm_ids, tok_ids)): + if spm_id != tok_id: + break + first = i + for i, (spm_id, tok_id) in enumerate(zip(reversed(spm_ids), reversed(tok_ids))): + if spm_id != tok_id: + break + last = len(spm_ids) - i + + spm_diff = spm_ids[first:last] + tok_diff = tok_ids[first:last] + + if check_diff(spm_diff, tok_diff, slow, fast): + return True + + if check_LTR_mark(line, first, fast): + return True + + if last - first > 5: + # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems + spms = Counter(spm_ids[first:last]) + toks = Counter(tok_ids[first:last]) + + removable_tokens = {spm_ for (spm_, si) in spms.items() if toks.get(spm_, 0) == si} + min_width = 3 + for i in range(last - first - min_width): + if all(spm_ids[first + i + j] in removable_tokens for j in range(min_width)): + possible_matches = [ + k + for k in range(last - first - min_width) + if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width] + ] + for j in possible_matches: + if check_diff(spm_ids[first : first + i], tok_ids[first : first + j], slow, fast) and check_details( + line, + spm_ids[first + i : last], + tok_ids[first + j : last], + slow, + fast, + ): + return True + + print(f"Spm: {[fast.decode([spm_ids[i]]) for i in range(first, last)]}") + try: + print(f"Tok: {[fast.decode([tok_ids[i]]) for i in range(first, last)]}") + except Exception: + pass + + fast.decode(spm_ids[:first]) + fast.decode(spm_ids[last:]) + wrong = fast.decode(spm_ids[first:last]) + print() + print(wrong) + return False + + +def test_string(slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase, text: str) -> None: + global perfect + global imperfect + global wrong + global total + + slow_ids = slow.encode(text) + fast_ids = fast.encode(text) + + skip_assert = False + total += 1 + + if slow_ids != fast_ids: + if check_details(text, slow_ids, fast_ids, slow, fast): + skip_assert = True + imperfect += 1 + else: + wrong += 1 + else: + perfect += 1 + + if total % 10000 == 0: + print(f"({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})") + + if skip_assert: + return + + assert ( + slow_ids == fast_ids + ), f"line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(text)}\n{fast.tokenize(text)}" + + +def test_tokenizer(slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase) -> None: + global batch_total + for i in range(len(dataset)): + # premise, all languages + for text in dataset[i]["premise"].values(): + test_string(slow, fast, text) + + # hypothesis, all languages + for text in dataset[i]["hypothesis"]["translation"]: + test_string(slow, fast, text) + + +if __name__ == "__main__": + for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items(): + checkpoint_names = list(slow_class.max_model_input_sizes.keys()) + for checkpoint in checkpoint_names: + imperfect = 0 + perfect = 0 + wrong = 0 + total = 0 + + print(f"========================== Checking {name}: {checkpoint} ==========================") + slow = slow_class.from_pretrained(checkpoint, force_download=True) + fast = fast_class.from_pretrained(checkpoint, force_download=True) + test_tokenizer(slow, fast) + print(f"Accuracy {perfect * 100 / total:.2f}") diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/scripts/distributed/torch-distributed-gpu-test.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/scripts/distributed/torch-distributed-gpu-test.py new file mode 100644 index 0000000000000000000000000000000000000000..22a99d570e4f85ba4fcbaf470fbbda1856fc8edd --- /dev/null +++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/scripts/distributed/torch-distributed-gpu-test.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python + +# +# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or +# many nodes) can talk to each other via nccl and allocate gpu memory. +# +# To run first adjust the number of processes and nodes: +# +# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py +# +# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port +# +# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d +# +# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9 +# +# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with: +# +# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py +# +# which should tell you what's going on behind the scenes. +# +# +# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that +# runs on 2 nodes of 4 gpus per node: +# +# #SBATCH --job-name=test-nodes # name +# #SBATCH --nodes=2 # nodes +# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node! +# #SBATCH --cpus-per-task=10 # number of cores per tasks +# #SBATCH --gres=gpu:4 # number of gpus +# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS) +# #SBATCH --output=%x-%j.out # output file name +# +# GPUS_PER_NODE=4 +# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1) +# MASTER_PORT=6000 +# +# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \ +# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \ +# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \ +# torch-distributed-gpu-test.py' +# + +import fcntl +import os +import socket + +import torch +import torch.distributed as dist + + +def printflock(*msgs): + """solves multi-process interleaved print problem""" + with open(__file__, "r") as fh: + fcntl.flock(fh, fcntl.LOCK_EX) + try: + print(*msgs) + finally: + fcntl.flock(fh, fcntl.LOCK_UN) + + +local_rank = int(os.environ["LOCAL_RANK"]) +torch.cuda.set_device(local_rank) +device = torch.device("cuda", local_rank) +hostname = socket.gethostname() + +gpu = f"[{hostname}-{local_rank}]" + +try: + # test distributed + dist.init_process_group("nccl") + dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM) + dist.barrier() + + # test cuda is available and can allocate memory + torch.cuda.is_available() + torch.ones(1).cuda(local_rank) + + # global rank + rank = dist.get_rank() + world_size = dist.get_world_size() + + printflock(f"{gpu} is OK (global rank: {rank}/{world_size})") + + dist.barrier() + if rank == 0: + printflock(f"pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}") + +except Exception: + printflock(f"{gpu} is broken") + raise diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/scripts/stale.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/scripts/stale.py new file mode 100644 index 0000000000000000000000000000000000000000..bf7c6670c431e152b63d3ea772e1a3523cee3d37 --- /dev/null +++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/scripts/stale.py @@ -0,0 +1,74 @@ +# Copyright 2021 The HuggingFace Team, the AllenNLP library authors. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Script to close stale issue. Taken in part from the AllenNLP repository. +https://github.com/allenai/allennlp. +""" +import os +from datetime import datetime as dt + +import github.GithubException +from github import Github + + +LABELS_TO_EXEMPT = [ + "good first issue", + "good second issue", + "good difficult issue", + "feature request", + "new model", + "wip", +] + + +def main(): + g = Github(os.environ["GITHUB_TOKEN"]) + repo = g.get_repo("huggingface/transformers") + open_issues = repo.get_issues(state="open") + + for i, issue in enumerate(open_issues): + print(i, issue) + comments = sorted(list(issue.get_comments()), key=lambda i: i.created_at, reverse=True) + last_comment = comments[0] if len(comments) > 0 else None + if ( + last_comment is not None and last_comment.user.login == "github-actions[bot]" + and (dt.utcnow() - issue.updated_at.replace(tzinfo=None)).days > 7 + and (dt.utcnow() - issue.created_at.replace(tzinfo=None)).days >= 30 + and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()) + ): + # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.") + try: + issue.edit(state="closed") + except github.GithubException as e: + print("Couldn't close the issue:", repr(e)) + elif ( + (dt.utcnow() - issue.updated_at.replace(tzinfo=None)).days > 23 + and (dt.utcnow() - issue.created_at.replace(tzinfo=None)).days >= 30 + and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()) + ): + # print(f"Would add stale comment to {issue.number}") + try: + issue.create_comment( + "This issue has been automatically marked as stale because it has not had " + "recent activity. If you think this still needs to be addressed " + "please comment on this thread.\n\nPlease note that issues that do not follow the " + "[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) " + "are likely to be ignored." + ) + except github.GithubException as e: + print("Couldn't create comment:", repr(e)) + + +if __name__ == "__main__": + main() diff --git a/tasks/tasksmith-5cbfd9f64dbc/tests/source/setup.py b/tasks/tasksmith-5cbfd9f64dbc/tests/source/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..4e641728b97c0730ec2e7def95ea50f17e717913 --- /dev/null +++ b/tasks/tasksmith-5cbfd9f64dbc/tests/source/setup.py @@ -0,0 +1,520 @@ +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/main/setup.py + +To create the package for pypi. + +1. Create the release branch named: v