Upload batch 113 (20 files, last=huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-vi-f50546-2087567168.md)

huggingface_dataset/Dataset_Card/AhmedSSabir_Japanese-wiki-dump-sentence-dataset.md

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+# Dataset 
+
+5M (5121625) clean Japanese full sentence with the context. This dataset can be used to learn unsupervised semantic similarity, etc.  

huggingface_dataset/Dataset_Card/Nerfgun3_Elden_Ring_Embeddings.md

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+---
+language:
+- en
+tags:
+- stable-diffusion
+- text-to-image
+license: creativeml-openrail-m
+inference: false
+
+---
+
+# Lands Between Elden Ring Embedding / Textual Inversion
+
+## Usage
+To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder
+
+Two different Versions:
+
+### Version 1:
+
+File: ```lands_between```
+
+To use it in a prompt: ```"art by lands_between"```
+
+For best use write something like ```highly detailed background art by lands_between```
+
+### Version 2:
+
+File: ```elden_ring```
+
+To use it in a prompt: ```"art by elden_ring"```
+
+For best use write something like ```highly detailed background art by elden_ring```
+
+
+If it is to strong just add [] around it.
+
+Trained until 7000 steps
+
+Have fun :)
+
+## Example Pictures
+
+<table>
+  <tr>
+    <td><img src=https://i.imgur.com/Pajrsvy.png width=100% height=100%/></td>
+    <td><img src=https://i.imgur.com/Bly3NJi.png width=100% height=100%/></td>
+    <td><img src=https://i.imgur.com/IxLNgB6.png width=100% height=100%/></td>
+   </tr>
+   <tr>
+    <td><img src=https://i.imgur.com/6rJ5ppD.png width=100% height=100%/></td>
+    <td><img src=https://i.imgur.com/ueTEHtb.png width=100% height=100%/></td>
+    <td><img src=https://i.imgur.com/dlVIwXs.png width=100% height=100%/></td>
+   </tr>
+</table>
+
+## License
+
+This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
+The CreativeML OpenRAIL License specifies: 
+
+1. You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content 
+2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
+3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
+[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

huggingface_dataset/Dataset_Card/and111_bert_pretrain_phase2.md

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+### Dataset Summary
+
+Input data for the **second** phase of BERT pretraining (sequence length 512). All text is tokenized with [bert-base-uncased](https://huggingface.co/bert-base-uncased) tokenizer. 
+Data is obtained by concatenating and shuffling [wikipedia](https://huggingface.co/datasets/wikipedia) (split: `20220301.en`) and [bookcorpusopen](https://huggingface.co/datasets/bookcorpusopen) datasets and running [reference BERT data preprocessor](https://github.com/google-research/bert/blob/master/create_pretraining_data.py) without masking and input duplication (`dupe_factor = 1`). Documents are split into sentences with the [NLTK](https://www.nltk.org/) sentence tokenizer (`nltk.tokenize.sent_tokenize`).
+
+See the dataset for the **first** phase of pretraining: [bert_pretrain_phase1](https://huggingface.co/datasets/and111/bert_pretrain_phase1).

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-futin__guess-vi-f50546-2087567168.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- futin/guess
+eval_info:
+  task: text_zero_shot_classification
+  model: bigscience/bloomz-1b1
+  metrics: []
+  dataset_name: futin/guess
+  dataset_config: vi
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: bigscience/bloomz-1b1
+* Dataset: futin/guess
+* Config: vi
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@futin](https://huggingface.co/futin) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-phpthinh__exampletx-constructive-666f04-1710259829.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- phpthinh/exampletx
+eval_info:
+  task: text_zero_shot_classification
+  model: gpt2
+  metrics: []
+  dataset_name: phpthinh/exampletx
+  dataset_config: constructive
+  dataset_split: test
+  col_mapping:
+    text: text
+    classes: classes
+    target: target
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Zero-Shot Text Classification
+* Model: gpt2
+* Dataset: phpthinh/exampletx
+* Config: constructive
+* Split: test
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model.

huggingface_dataset/Dataset_Card/autoevaluate_autoeval-eval-squad_v2-squad_v2-d6c6f4-2395874932.md

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+---
+type: predictions
+tags:
+- autotrain
+- evaluation
+datasets:
+- squad_v2
+eval_info:
+  task: extractive_question_answering
+  model: mlxen/electra-smallcase-squad
+  metrics: []
+  dataset_name: squad_v2
+  dataset_config: squad_v2
+  dataset_split: validation
+  col_mapping:
+    context: context
+    question: question
+    answers-text: answers.text
+    answers-answer_start: answers.answer_start
+---
+# Dataset Card for AutoTrain Evaluator
+
+This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
+
+* Task: Question Answering
+* Model: mlxen/electra-smallcase-squad
+* Dataset: squad_v2
+* Config: squad_v2
+* Split: validation
+
+To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
+
+## Contributions
+
+Thanks to [@ob](https://huggingface.co/ob) for evaluating this model.

huggingface_dataset/Dataset_Card/bigbio_psytar.md

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+
+---
+language: 
+- en
+bigbio_language: 
+- English
+license: cc-by-4.0
+multilinguality: monolingual
+bigbio_license_shortname: CC_BY_4p0
+pretty_name: PsyTAR
+homepage: https://www.askapatient.com/research/pharmacovigilance/corpus-ades-psychiatric-medications.asp
+bigbio_pubmed: False
+bigbio_public: False
+bigbio_tasks: 
+- NAMED_ENTITY_RECOGNITION
+- TEXT_CLASSIFICATION
+---
+
+
+# Dataset Card for PsyTAR
+
+## Dataset Description
+
+- **Homepage:** https://www.askapatient.com/research/pharmacovigilance/corpus-ades-psychiatric-medications.asp
+- **Pubmed:** False
+- **Public:** False
+- **Tasks:** NER,TXTCLASS
+
+
+The "Psychiatric Treatment Adverse Reactions" (PsyTAR) dataset contains 891 drugs
+reviews posted by patients on "askapatient.com", about the effectiveness and adverse
+drug events associated with Zoloft, Lexapro, Cymbalta, and Effexor XR.
+
+This dataset can be used for (multi-label) sentence classification of Adverse Drug
+Reaction (ADR), Withdrawal Symptoms (WDs), Sign/Symptoms/Illness (SSIs), Drug
+Indications (DIs), Drug Effectiveness (EF), Drug Infectiveness (INF) and Others, as well
+as for recognition of 5 different types of named entity (in the categories ADRs, WDs,
+SSIs and DIs)
+
+
+
+## Citation Information
+
+```
+@article{Zolnoori2019,
+  author    = {Maryam Zolnoori and
+               Kin Wah Fung and
+               Timothy B. Patrick and
+               Paul Fontelo and
+               Hadi Kharrazi and
+               Anthony Faiola and
+               Yi Shuan Shirley Wu and
+               Christina E. Eldredge and
+               Jake Luo and
+               Mike Conway and
+               Jiaxi Zhu and
+               Soo Kyung Park and
+               Kelly Xu and
+               Hamideh Moayyed and
+               Somaieh Goudarzvand},
+  title     = {A systematic approach for developing a corpus of patient                reported adverse drug events: A case study for {SSRI} and {SNRI} medications},
+  journal   = {Journal of Biomedical Informatics},
+  volume    = {90},
+  year      = {2019},
+  url       = {https://doi.org/10.1016/j.jbi.2018.12.005},
+  doi       = {10.1016/j.jbi.2018.12.005},
+}
+
+```

huggingface_dataset/Dataset_Card/conceptual_captions.md

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+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+task_categories:
+- image-to-text
+task_ids:
+- image-captioning
+paperswithcode_id: conceptual-captions
+pretty_name: Conceptual Captions
+dataset_info:
+- config_name: default
+  features:
+  - name: id
+    dtype: string
+  - name: caption
+    dtype: string
+  - name: url
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 623230370
+    num_examples: 3318333
+  - name: validation
+    num_bytes: 2846024
+    num_examples: 15840
+  download_size: 0
+  dataset_size: 626076394
+- config_name: unlabeled
+  features:
+  - name: image_url
+    dtype: string
+  - name: caption
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 584520156
+    num_examples: 3318333
+  - name: validation
+    num_bytes: 2698726
+    num_examples: 15840
+  download_size: 567211172
+  dataset_size: 587218882
+- config_name: labeled
+  features:
+  - name: image_url
+    dtype: string
+  - name: caption
+    dtype: string
+  - name: labels
+    sequence: string
+  - name: MIDs
+    sequence: string
+  - name: confidence_scores
+    sequence: float64
+  splits:
+  - name: train
+    num_bytes: 1199330856
+    num_examples: 2007090
+  download_size: 1282463277
+  dataset_size: 1199330856
+---
+
+# Dataset Card for Conceptual Captions
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Dataset Preprocessing](#dataset-preprocessing)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [Conceptual Captions homepage](https://ai.google.com/research/ConceptualCaptions/)
+- **Repository:** [Conceptual Captions repository](https://github.com/google-research-datasets/conceptual-captions)
+- **Paper:** [Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://www.aclweb.org/anthology/P18-1238/)
+- **Leaderboard:** [Conceptual Captions leaderboard](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard)https://ai.google.com/research/ConceptualCaptions/leaderboard?active_tab=leaderboard
+- **Point of Contact:** [Conceptual Captions e-mail](mailto:conceptual-captions@google.com)
+
+### Dataset Summary
+
+Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions.
+
+### Dataset Preprocessing
+
+This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
+
+```python
+from concurrent.futures import ThreadPoolExecutor
+from functools import partial
+import io
+import urllib
+
+import PIL.Image
+
+from datasets import load_dataset
+from datasets.utils.file_utils import get_datasets_user_agent
+
+
+USER_AGENT = get_datasets_user_agent()
+
+
+def fetch_single_image(image_url, timeout=None, retries=0):
+    for _ in range(retries + 1):
+        try:
+            request = urllib.request.Request(
+                image_url,
+                data=None,
+                headers={"user-agent": USER_AGENT},
+            )
+            with urllib.request.urlopen(request, timeout=timeout) as req:
+                image = PIL.Image.open(io.BytesIO(req.read()))
+            break
+        except Exception:
+            image = None
+    return image
+
+
+def fetch_images(batch, num_threads, timeout=None, retries=0):
+    fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
+    with ThreadPoolExecutor(max_workers=num_threads) as executor:
+        batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
+    return batch
+
+
+num_threads = 20
+dset = load_dataset("conceptual_captions")
+dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
+```
+
+### Supported Tasks and Leaderboards
+
+- `image-captioning`: This dataset can be used to train model for the Image Captioning task. The leaderboard for this task is available [here](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard). Official submission output captions are scored against the reference captions from the hidden test set using [this](https://github.com/tylin/coco-caption) implementation of the CIDEr (primary), ROUGE-L and SPICE metrics.
+
+### Languages
+
+All captions are in English.
+
+## Dataset Structure
+
+### Data Instances
+
+#### `unlabeled`
+
+Each instance in this configuration represents a single image with a caption:
+
+```
+{
+  'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800',
+  'caption': 'a very typical bus station'
+}
+```
+
+#### `labeled`
+
+Each instance in this configuration represents a single image with a caption with addtional machine-generated image labels and confidence scores:
+
+```
+{
+  'image_url': 'https://thumb1.shutterstock.com/display_pic_with_logo/261388/223876810/stock-vector-christmas-tree-on-a-black-background-vector-223876810.jpg',
+  'caption': 'christmas tree on a black background .',
+  'labels': ['christmas tree', 'christmas decoration', 'font', 'text', 'graphic design', 'illustration','interior design', 'tree', 'christmas eve', 'ornament', 'fir', 'plant', 'pine', 'pine family', 'graphics'],
+  'MIDs': ['/m/025nd', '/m/05fc9mj', '/m/03gq5hm', '/m/07s6nbt', '/m/03c31', '/m/01kr8f', '/m/0h8nzzj', '/m/07j7r', '/m/014r1s', '/m/05ykl4', '/m/016x4z', '/m/05s2s', '/m/09t57', '/m/01tfm0', '/m/021sdg'],
+  'confidence_scores': [0.9818305373191833, 0.952756941318512, 0.9227379560470581, 0.8524878621101379, 0.7597672343254089, 0.7493422031402588, 0.7332468628883362, 0.6869218349456787, 0.6552258133888245, 0.6357356309890747, 0.5992692708969116, 0.585474967956543, 0.5222904086112976, 0.5113164782524109, 0.5036579966545105]
+}
+```
+
+### Data Fields
+
+#### `unlabeled`
+
+- `image_url`: Static URL for downloading the image associated with the post.
+- `caption`: Textual description of the image.
+
+#### `labeled`
+
+- `image_url`: Static URL for downloading the image associated with the post.
+- `caption`: Textual description of the image.
+- `labels`: A sequence of machine-generated labels obtained using the [Google Cloud Vision API](https://cloud.google.com/vision).
+- `MIDs`:  A sequence of machine-generated identifiers (MID) corresponding to the label's Google Knowledge Graph entry.
+- `confidence_scores`: A sequence of confidence scores denoting how likely the corresponing labels are present on the image.
+
+### Data Splits
+
+#### `unlabeled`
+
+The basic version of the dataset split into Training and Validation splits. The Training split consists of 3,318,333 image-URL/caption pairs and the Validation split consists of 15,840 image-URL/caption pairs.
+
+#### `labeled`
+
+The labeled version of the dataset with a single. The entire data is contained in Training split, which is a subset of 2,007,090 image-URL/caption pairs from the Training set of the `unlabeled` config.
+
+## Dataset Creation
+
+### Curation Rationale
+
+From the paper:
+> In this paper, we make contributions to both the data and modeling categories. First, we present a new dataset of caption annotations Conceptual Captions (Fig. 1), which has an order of magnitude more images than the COCO dataset. Conceptual Captions consists of about 3.3M himage, descriptioni pairs. In contrast with the curated style of the COCO images, Conceptual Captions images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+From the homepage:
+>For Conceptual Captions, we developed a fully automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. Because no human annotators are involved, the Conceptual Captions dataset generation process is highly scalable.
+>
+>To generate this dataset, we started with a Flume pipeline that processes billions of Internet webpages, extracting, filtering, and processing candidate image and caption pairs, and keeping those that pass through several filters.
+>
+>We first screen for certain properties like size, aspect ratio, adult content scores. These filters discard more than 65% of the candidates. Next, we use Alt-Texts for text-based filtering, removing captions with non-descriptive text (such as SEO tags or hashtags); we also discard texts with high sentiment polarity or adult content scores, resulting in just 3% of the incoming candidates passing through.
+>
+>In the next step, we filter out candidates for which none of the text tokens can be mapped to the visual content of the image. We use image classifiers (e.g., Google Cloud Vision APIs) to assign class labels to images and match these labels against the candidate text (allowing morphological transformations), discarding >around 60% of the candidates that reach this stage.
+>
+>The candidates passing the above filters tend to be good Alt-text image descriptions. However, a large majority of these use proper names (for people, venues, locations, etc.), brands, dates, quotes, etc. This creates two distinct problems. First, some of these cannot be inferred based on the image pixels alone. This is problematic because unless the image has the necessary visual information it is not useful for training. Second, even if the proper names could be inferred from the image it is extremely difficult for a model to learn to perform both fine-grained classification and natural-language descriptions simultaneously. We posit that if automatic determination of names, locations, brands, etc. is needed, it should be done as a separate task that may leverage image meta-information (e.g. GPS info), or complementary techniques such as OCR.
+>
+>We address the above problems with the insight that proper names should be replaced by words that represent the same general notion, i.e., by their concept. For example, we remove locations (“Crowd at a concert in Los Angeles“ becomes “Crowd at a concert”), names (e.g., “Former Miss World Priyanka Chopra on the red carpet” becomes “actor on the red carpet”), proper noun modifiers (e.g., “Italian cuisine” becomes just “cuisine”) and noun phrases (e.g., “actor and actor” becomes “actors”). Around 20% of the samples are discarded during this transformation because it can leave sentences too short, or otherwise inconsistent.
+>
+>Finally, we perform another round of filtering to identify concepts with low-count. We cluster all resolved entities (e.g., “actor”, “dog”, “neighborhood”, etc.) and keep only the candidate types which have a count of over 100 mentions. This retains around 16K entity concepts such as: “person”, “actor”, “artist”, “player” and “illustration”. The less frequent ones that we dropped include “baguette”, “bridle”, “deadline”, “ministry” and “funnel”.
+
+#### Who are the source language producers?
+
+Not specified.
+
+### Annotations
+
+#### Annotation process
+
+Annotations are extracted jointly with the images using the automatic pipeline.
+
+#### Who are the annotators?
+
+Not specified.
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+Piyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut.
+
+### Licensing Information
+
+The dataset may be freely used for any purpose, although acknowledgement of
+Google LLC ("Google") as the data source would be appreciated. The dataset is
+provided "AS IS" without any warranty, express or implied. Google disclaims all
+liability for any damages, direct or indirect, resulting from the use of the
+dataset.
+
+### Citation Information
+
+```bibtex
+@inproceedings{sharma2018conceptual,
+  title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning},
+  author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
+  booktitle = {Proceedings of ACL},
+  year = {2018},
+}
+```
+
+### Contributions
+
+Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) and [@mariosasko](https://github.com/mariosasko) for adding this dataset.

huggingface_dataset/Dataset_Card/emreisik_news.md

@@ -0,0 +1,14 @@
+---
+license: bsd
+task_categories:
+- text-generation
+language:
+- tr
+pretty_name: News
+size_categories:
+- 1K<n<10K
+---
+This is the reporsitory of Turkish fake news dataset which consists of Zaytung posts and Hurriyet news articles.
+Code folder contains the web scrapper python files.
+Raw folder contains txt files downloaded from sources.
+Clean folder contains txt files in lowercase, punctuation and numbers removed.

huggingface_dataset/Dataset_Card/event2Mind.md

@@ -0,0 +1,220 @@
+---
+annotations_creators:
+- crowdsourced
+language:
+- en
+language_creators:
+- found
+license:
+- unknown
+multilinguality:
+- monolingual
+pretty_name: Event2Mind
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- text2text-generation
+task_ids: []
+paperswithcode_id: event2mind
+tags:
+- common-sense-inference
+dataset_info:
+  features:
+  - name: Source
+    dtype: string
+  - name: Event
+    dtype: string
+  - name: Xintent
+    dtype: string
+  - name: Xemotion
+    dtype: string
+  - name: Otheremotion
+    dtype: string
+  - name: Xsent
+    dtype: string
+  - name: Osent
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 649273
+    num_examples: 5221
+  - name: train
+    num_bytes: 5916384
+    num_examples: 46472
+  - name: validation
+    num_bytes: 672365
+    num_examples: 5401
+  download_size: 1300770
+  dataset_size: 7238022
+---
+
+# Dataset Card for "event2Mind"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://uwnlp.github.io/event2mind/](https://uwnlp.github.io/event2mind/)
+- **Repository:** https://github.com/uwnlp/event2mind
+- **Paper:** [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://arxiv.org/abs/1805.06939)
+- **Point of Contact:** [Hannah Rashkin](mailto:hrashkin@cs.washington.edu), [Maarten Sap](mailto:msap@cs.washington.edu)
+- **Size of downloaded dataset files:** 1.24 MB
+- **Size of the generated dataset:** 6.90 MB
+- **Total amount of disk used:** 8.14 MB
+
+### Dataset Summary
+
+In Event2Mind, we explore the task of understanding stereotypical intents and reactions to events. Through crowdsourcing, we create a large corpus with 25,000 events and free-form descriptions of their intents and reactions, both of the event's subject and (potentially implied) other participants.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Languages
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Dataset Structure
+
+### Data Instances
+
+#### default
+
+- **Size of downloaded dataset files:** 1.24 MB
+- **Size of the generated dataset:** 6.90 MB
+- **Total amount of disk used:** 8.14 MB
+
+An example of 'validation' looks as follows.
+```
+{
+    "Event": "It shrinks in the wash",
+    "Osent": "1",
+    "Otheremotion": "[\"upset\", \"angry\"]",
+    "Source": "it_events",
+    "Xemotion": "[\"none\"]",
+    "Xintent": "[\"none\"]",
+    "Xsent": ""
+}
+```
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+#### default
+- `Source`: a `string` feature.
+- `Event`: a `string` feature.
+- `Xintent`: a `string` feature.
+- `Xemotion`: a `string` feature.
+- `Otheremotion`: a `string` feature.
+- `Xsent`: a `string` feature.
+- `Osent`: a `string` feature.
+
+### Data Splits
+
+| name  |train|validation|test|
+|-------|----:|---------:|---:|
+|default|46472|      5401|5221|
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Citation Information
+
+```
+@inproceedings{rashkin-etal-2018-event2mind,
+    title = "{E}vent2{M}ind: Commonsense Inference on Events, Intents, and Reactions",
+    author = "Rashkin, Hannah  and
+      Sap, Maarten  and
+      Allaway, Emily  and
+      Smith, Noah A.  and
+      Choi, Yejin",
+    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2018",
+    address = "Melbourne, Australia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P18-1043",
+    doi = "10.18653/v1/P18-1043",
+    pages = "463--473",
+}
+```
+
+### Contributions
+
+Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset.

huggingface_dataset/Dataset_Card/gutenberg_time.md

@@ -0,0 +1,190 @@
+---
+annotations_creators:
+- crowdsourced
+language_creators:
+- found
+language:
+- en
+license:
+- unknown
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<1M
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids:
+- multi-class-classification
+paperswithcode_id: gutenberg-time-dataset
+pretty_name: the Gutenberg Time dataset
+dataset_info:
+  features:
+  - name: guten_id
+    dtype: string
+  - name: hour_reference
+    dtype: string
+  - name: time_phrase
+    dtype: string
+  - name: is_ambiguous
+    dtype: bool_
+  - name: time_pos_start
+    dtype: int64
+  - name: time_pos_end
+    dtype: int64
+  - name: tok_context
+    dtype: string
+  config_name: gutenberg
+  splits:
+  - name: train
+    num_bytes: 108550391
+    num_examples: 120694
+  download_size: 35853781
+  dataset_size: 108550391
+---
+
+# Dataset Card for the Gutenberg Time dataset
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **[Repository](https://github.com/allenkim/what-time-is-it)**
+- **[Paper](https://arxiv.org/abs/2011.04124)**
+
+### Dataset Summary
+
+A clean data resource containing all explicit time references in a dataset of 52,183 novels whose full text is available via Project Gutenberg.
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+Time-of-the-day classification from excerpts.
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{
+    "guten_id": 28999,
+    "hour_reference": 12,
+    "time_phrase": "midday",
+    "is_ambiguous": False,
+    "time_pos_start": 133,
+    "time_pos_end": 134,
+    "tok_context": "Sorrows and trials she had had in plenty in her life , but these the sweetness of her nature had transformed , so that from being things difficult to bear , she had built up with them her own character . Sorrow had increased her own power of sympathy ; out of trials she had learnt patience ; and failure and the gradual sinking of one she had loved into the bottomless slough of evil habit had but left her with an added dower of pity and tolerance . So the past had no sting left , and if iron had ever entered into her soul it now but served to make it strong . She was still young , too ; it was not near sunset with her yet , nor even midday , and the future that , humanly speaking , she counted to be hers was almost dazzling in its brightness . For love had dawned for her again , and no uncertain love , wrapped in the mists of memory , but one that had ripened through liking and friendship and intimacy into the authentic glory . He was in England , too ; she was going back to him . And before very long she would never go away from him again ."
+}
+```
+
+### Data Fields
+
+```
+    guten_id - Gutenberg ID number
+    hour_reference - hour from 0 to 23
+    time_phrase - the phrase corresponding to the referenced hour
+    is_ambiguous - boolean whether it is clear whether time is AM or PM
+    time_pos_start - token position where time_phrase begins
+    time_pos_end - token position where time_phrase ends (exclusive)
+    tok_context - context in which time_phrase appears as space-separated tokens
+```
+
+### Data Splits
+
+No data splits.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The flow of time is an indispensable guide for our actions, and provides a framework in which to see a logical progression of events. Just as in real life,the clock provides the background against which literary works play out: when characters wake, eat,and act. In most works of fiction, the events of the story take place during recognizable time periods over the course of the day. Recognizing a story’s flow through time is essential to understanding the text.In this paper, we try to capture the flow of time through novels by attempting to recognize what time of day each event in the story takes place at.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+Novel authors.
+
+### Annotations
+
+#### Annotation process
+
+Manually annotated.
+
+#### Who are the annotators?
+
+Two of the authors.
+
+### Personal and Sensitive Information
+
+No Personal or sensitive information.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+Allen Kim, Charuta Pethe and Steven Skiena, Stony Brook University
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@misc{kim2020time,
+      title={What time is it? Temporal Analysis of Novels}, 
+      author={Allen Kim and Charuta Pethe and Steven Skiena},
+      year={2020},
+      eprint={2011.04124},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+### Contributions
+
+Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset.

huggingface_dataset/Dataset_Card/hackathon-pln-es_biomed_squad_es_v2.md

@@ -0,0 +1,84 @@
+# Dataset Card for biomed_squad_es_v2
+
+This Dataset was created as part of the "Extractive QA Biomedicine" project developed during the 2022 [Hackathon](https://somosnlp.org/hackathon) organized by SOMOS NLP.
+
+## Table of Contents
+
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  
+## Dataset Description
+
+- **Homepage:** [Needs More Information]
+- **Repository:** [Needs More Information]
+- **Paper:** [Needs More Information]
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Needs More Information]
+
+### Dataset Summary
+
+This is a subset of the [dev squad_es (v2) dataset](https://huggingface.co/datasets/squad_es) (automatic translation of the Stanford Question Answering Dataset v2 into Spanish) containing questions related to the biomedical domain.
+
+License, distribution and usage conditions of the original Squad_es Dataset apply.
+
+### Languages
+
+Spanish
+
+## Dataset Structure
+
+### Data Fields
+
+```
+{'answers': {'answer_start': [343, 343, 343],
+  'text': ['diez veces su propio peso',
+   'diez veces su propio peso',
+   'diez veces su propio peso']},
+ 'context': 'Casi todos los ctenóforos son depredadores, tomando presas que van desde larvas microscópicas y rotíferos a los adultos de pequeños crustáceos; Las excepciones son los juveniles de dos especies, que viven como parásitos en las salpas en las que los adultos de su especie se alimentan. En circunstancias favorables, los ctenóforos pueden comer diez veces su propio peso en un día. Sólo 100-150 especies han sido validadas, y posiblemente otras 25 no han sido completamente descritas y nombradas. Los ejemplos de libros de texto son cidipidos con cuerpos en forma de huevo y un par de tentáculos retráctiles bordeados con tentilla ("pequeños tentáculos") que están cubiertos con colúnculos, células pegajosas. El filo tiene una amplia gama de formas corporales, incluyendo los platyctenidos de mar profundo, en los que los adultos de la mayoría de las especies carecen de peines, y los beroides costeros, que carecen de tentáculos. Estas variaciones permiten a las diferentes especies construir grandes poblaciones en la misma área, porque se especializan en diferentes tipos de presas, que capturan por una amplia gama de métodos que utilizan las arañas.',
+ 'id': '5725c337271a42140099d165',
+ 'question': '¿Cuánta comida come un Ctenophora en un día?',
+ 'title': 'Ctenophora'}
+```
+
+### Data Splits
+
+Validation: 1137 examples
+
+
+### Citation Information
+
+```
+@article{2016arXiv160605250R,
+       author = {Casimiro Pio , Carrino and  Marta R. , Costa-jussa and  Jose A. R. , Fonollosa},
+        title = "{Automatic Spanish Translation of the SQuAD Dataset for Multilingual
+Question Answering}",
+      journal = {arXiv e-prints},
+         year = 2019,
+          eid = {arXiv:1912.05200v1},
+        pages = {arXiv:1912.05200v1},
+archivePrefix = {arXiv},
+       eprint = {1912.05200v2},
+}
+```
+
+## Team
+Santiago Maximo: [smaximo](https://huggingface.co/smaximo)

huggingface_dataset/Dataset_Card/huggingface_label-files.md

@@ -0,0 +1,39 @@
+This repository contains the mapping from integer id's to actual label names (in HuggingFace Transformers typically called `id2label`) for several datasets.
+
+Current datasets include:
+- ImageNet-1k
+- ImageNet-22k (also called ImageNet-21k as there are 21,843 classes)
+- COCO detection 2017
+- COCO panoptic 2017
+- ADE20k (actually, the [MIT Scene Parsing benchmark](http://sceneparsing.csail.mit.edu/), which is a subset of ADE20k)
+- Cityscapes
+- VQAv2
+- Kinetics-700
+- RVL-CDIP
+- PASCAL VOC
+- Kinetics-400
+- ...
+
+You can read in a label file as follows (using the `huggingface_hub` library):
+
+```
+from huggingface_hub import hf_hub_download
+import json
+
+repo_id = "huggingface/label-files"
+filename = "imagenet-22k-id2label.json"
+id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r"))
+id2label = {int(k):v for k,v in id2label.items()}
+```
+
+To add an `id2label` mapping for a new dataset, simply define a Python dictionary, and then save that dictionary as a JSON file, like so:
+```
+import json
+
+# simple example
+id2label = {0: 'cat', 1: 'dog'}
+
+with open('cats-and-dogs-id2label.json', 'w') as fp:
+    json.dump(id2label, fp)
+```
+You can then upload it to this repository (assuming you have write access).

huggingface_dataset/Dataset_Card/irds_neumarco_zh.md

@@ -0,0 +1,35 @@
+---
+pretty_name: '`neumarco/zh`'
+viewer: false
+source_datasets: []
+task_categories:
+- text-retrieval
+---
+
+# Dataset Card for `neumarco/zh`
+
+The `neumarco/zh` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
+For more information about the dataset, see the [documentation](https://ir-datasets.com/neumarco#neumarco/zh).
+
+# Data
+
+This dataset provides:
+ - `docs` (documents, i.e., the corpus); count=8,841,823
+
+
+This dataset is used by: [`neumarco_zh_dev`](https://huggingface.co/datasets/irds/neumarco_zh_dev), [`neumarco_zh_dev_judged`](https://huggingface.co/datasets/irds/neumarco_zh_dev_judged), [`neumarco_zh_dev_small`](https://huggingface.co/datasets/irds/neumarco_zh_dev_small), [`neumarco_zh_train`](https://huggingface.co/datasets/irds/neumarco_zh_train), [`neumarco_zh_train_judged`](https://huggingface.co/datasets/irds/neumarco_zh_train_judged)
+
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+docs = load_dataset('irds/neumarco_zh', 'docs')
+for record in docs:
+    record # {'doc_id': ..., 'text': ...}
+
+```
+
+Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
+data in 🤗 Dataset format.

huggingface_dataset/Dataset_Card/lewtun_raft-test-submission.md

@@ -0,0 +1,126 @@
+---
+benchmark: raft
+type: prediction
+submission_name: Test submission 0
+---
+
+# RAFT submissions for raft-test-submission
+
+## Submitting to the leaderboard
+
+To make a submission to the [leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard), there are three main steps:
+
+1. Generate predictions on the unlabeled test set of each task
+2. Validate the predictions are compatible with the evaluation framework
+3. Push the predictions to the Hub!
+
+See the instructions below for more details.
+
+### Rules
+
+1. To prevent overfitting to the public leaderboard, we only evaluate **one submission per week**. You can push predictions to the Hub as many times as you wish, but we will only evaluate the most recent commit in a given week. 
+2. Transfer or meta-learning using other datasets, including further pre-training on other corpora, is allowed.
+3. Use of unlabeled test data is allowed, as is it always available in the applied setting. For example, further pre-training using the unlabeled data for a task would be permitted.
+4. Systems may be augmented with information retrieved from the internet, e.g. via automated web searches.
+
+### Submission file format
+
+For each task in RAFT, you should create a CSV file called `predictions.csv` with your model's predictions on the unlabeled test set. Each file should have exactly 2 columns:
+
+* ID (int)
+* Label (string)
+
+See the dummy predictions in the `data` folder for examples with the expected format. Here is a simple example that creates a majority-class baseline:
+
+```python
+from pathlib import Path
+import pandas as pd
+from collections import Counter
+from datasets import load_dataset, get_dataset_config_names
+
+tasks = get_dataset_config_names("ought/raft")
+
+for task in tasks:
+    # Load dataset
+    raft_subset = load_dataset("ought/raft", task)
+    # Compute majority class over training set
+    counter = Counter(raft_subset["train"]["Label"])
+    majority_class = counter.most_common(1)[0][0]
+    # Load predictions file
+    preds = pd.read_csv(f"data/{task}/predictions.csv")
+    # Convert label IDs to label names
+    preds["Label"] = raft_subset["train"].features["Label"].int2str(majority_class)
+    # Save predictions
+    preds.to_csv(f"data/{task}/predictions.csv", index=False)
+```
+
+As you can see in the example, each `predictions.csv` file should be stored in the task's subfolder in `data` and at the end you should have something like the following:
+
+```
+data
+├── ade_corpus_v2
+│   ├── predictions.csv
+│   └── task.json
+├── banking_77
+│   ├── predictions.csv
+│   └── task.json
+├── neurips_impact_statement_risks
+│   ├── predictions.csv
+│   └── task.json
+├── one_stop_english
+│   ├── predictions.csv
+│   └── task.json
+├── overruling
+│   ├── predictions.csv
+│   └── task.json
+├── semiconductor_org_types
+│   ├── predictions.csv
+│   └── task.json
+├── systematic_review_inclusion
+│   ├── predictions.csv
+│   └── task.json
+├── tai_safety_research
+│   ├── predictions.csv
+│   └── task.json
+├── terms_of_service
+│   ├── predictions.csv
+│   └── task.json
+├── tweet_eval_hate
+│   ├── predictions.csv
+│   └── task.json
+└── twitter_complaints
+    ├── predictions.csv
+    └── task.json
+```
+
+### Validate your submission
+
+To ensure that your submission files are correctly formatted, run the following command from the root of the repository:
+
+```
+python cli.py validate
+```
+
+If everything is correct, you should see the following message:
+
+```
+All submission files validated! ✨ 🚀 ✨
+Now you can make a submission 🤗
+```
+
+### Push your submission to the Hugging Face Hub!
+
+The final step is to commit your files and push them to the Hub:
+
+```
+python cli.py submit
+```
+
+If there are no errors, you should see the following message:
+
+```
+Submission successful! 🎉 🥳 🎉
+Your submission will be evaulated on Sunday 05 September 2021 ⏳
+```
+
+where the evaluation is run every Sunday and your results will be visible on the leaderboard.

huggingface_dataset/Dataset_Card/nedjmaou_MLMA_hate_speech.md

@@ -0,0 +1,48 @@
+---
+license: mit
+---
+# Disclaimer
+*This is a hate speech dataset (in Arabic, French, and English).*
+
+*Offensive content that does not reflect the opinions of the authors.* 
+
+# Dataset of our EMNLP 2019 Paper (Multilingual and Multi-Aspect Hate Speech Analysis)
+For more details about our dataset, please check our paper:
+
+	@inproceedings{ousidhoum-etal-multilingual-hate-speech-2019,
+    		title = "Multilingual and Multi-Aspect Hate Speech Analysis",
+    		author = "Ousidhoum, Nedjma
+             		and Lin, Zizheng
+             		and Zhang, Hongming
+            		and Song, Yangqiu
+            		and Yeung, Dit-Yan",
+    			booktitle = "Proceedings of EMNLP",
+    		year = "2019",
+    		publisher =	"Association for Computational Linguistics",
+	}	
+
+(You can preview our paper on https://arxiv.org/pdf/1908.11049.pdf)
+
+## Clarification
+The multi-labelled tasks are *the hostility type of the tweet* and the *annotator's sentiment*. (We kept labels on which at least two annotators agreed.)
+
+## Taxonomy
+In further experiments that involved binary classification tasks of the hostility/hate/abuse type, we considered single-labelled *normal* instances to be *non-hate/non-toxic* and all the other instances to be *toxic*.
+
+## Dataset
+Our dataset is composed of three csv files sorted by language. They contain the tweets and the annotations described in our paper:
+
+the hostility type *(column: tweet sentiment)* 
+
+hostility directness *(column: directness)* 
+
+target attribute *(column: target)*
+
+target group *(column: group)* 
+
+annotator's sentiment *(column: annotator sentiment)*.
+
+## Experiments
+
+To replicate our experiments, please see https://github.com/HKUST-KnowComp/MLMA_hate_speech/blob/master/README.md 
+

huggingface_dataset/Dataset_Card/p1atdev_nobodies.md

@@ -0,0 +1,35 @@
+---
+license: cc0-1.0
+---
+
+# Nobodies
+
+AI-generated human image dataset.
+
+## Contents
+
+### Face
+
+- [vol1](https://huggingface.co/datasets/p1atdev/nobodies/blob/main/face/vol1.zip): 32 photos of women's faces. Generated with [WD1.5 beta 2](https://huggingface.co/waifu-diffusion/wd-1-5-beta2).
+
+Sample:
+
+<img class="max-w-lg" src="https://huggingface.co/datasets/p1atdev/nobodies/resolve/main/samples/face/vol1.jpg" />
+
+### Portrait
+
+- [vol1](https://huggingface.co/datasets/p1atdev/nobodies/blob/main/portrait/vol1.zip): 31 photos of women's portraits. Generated with [WD1.5 beta 2](https://huggingface.co/waifu-diffusion/wd-1-5-beta2) and the [fashion LoCon](https://huggingface.co/p1atdev/lora/blob/main/fashion-test1-e5.safetensors).
+
+Sample:
+
+<img class="max-w-lg" src="https://huggingface.co/datasets/p1atdev/nobodies/resolve/main/samples/portrait/vol1.jpg" />
+
+- [vol2](https://huggingface.co/datasets/p1atdev/nobodies/blob/main/portrait/vol2.zip): 165 photos of woman's portraits. Generated with [WD1.5 beta 2](https://huggingface.co/waifu-diffusion/wd-1-5-beta2) and the [fashion LoCon](https://huggingface.co/p1atdev/lora/blob/main/fashion-test1-e5.safetensors). Classified with LAION Aesthetic v2.
+  - 75 hair bun photos
+  - 90 medium hair photos
+
+<div class="flex overflow-scroll">
+<img class="max-w-lg" src="https://huggingface.co/datasets/p1atdev/nobodies/resolve/main/samples/portrait/vol2-a.jpg" />
+<img class="max-w-lg" src="https://huggingface.co/datasets/p1atdev/nobodies/resolve/main/samples/portrait/vol2-b.jpg" />
+</div>
+

huggingface_dataset/Dataset_Card/pixta-ai_Plane-images-in-multiple-scenes.md

@@ -0,0 +1,32 @@
+---
+YAML tags:
+- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
+---
+
+# Dataset Card for pixta-ai/Plane-images-in-multiple-scenes
+
+## Dataset Description
+
+- **Homepage:** https://www.pixta.ai/
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:** 
+
+### Dataset Summary
+
+4,000 Plane images in multiple scenes, including multiple types of planes disproportionately, the passenger plan are the majorities.
+Each image contains from 1 to 10 visible planes
+                                                                                                                                  
+For more details, please refer to the link: https://www.pixta.ai/
+Or send your inquiries to contact@pixta.ai 
+
+### Supported Tasks and Leaderboards
+
+object-detection, computer-vision: The dataset can be used to train or enhance model for object detection.
+
+### Languages
+English
+
+### License
+Academic & commercial usage

huggingface_dataset/Dataset_Card/wiki_source.md

@@ -0,0 +1,165 @@
+---
+annotations_creators:
+- found
+language_creators:
+- found
+language:
+- en
+- sv
+license:
+- unknown
+multilinguality:
+- multilingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- translation
+task_ids: []
+paperswithcode_id: null
+pretty_name: WikiSource
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: translation
+    dtype:
+      translation:
+        languages:
+        - en
+        - sv
+  config_name: en-sv
+  splits:
+  - name: train
+    num_bytes: 8153542
+    num_examples: 33283
+  download_size: 2375052
+  dataset_size: 8153542
+---
+
+# Dataset Card for WikiSource
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** http://opus.nlpl.eu/WikiSource.php
+- **Repository:** None
+- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
+- **Leaderboard:** [More Information Needed]
+- **Point of Contact:** [More Information Needed]
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.

huggingface_dataset/Dataset_Card/xzyao_HC3-Evaluation.md

@@ -0,0 +1,8 @@
+## Metrics
+
+|Metric/Model| ChatGPT  |  sanagnos/galactica-6.7b-finetuned | NeoX-Soda |
+|---|---|---|---|
+|rouge1| 0.2865  |  0.1513 ||
+|Rouge2| 0.05863  |  0.0311 ||
+|rougeL| 0.1519  |  0.1065 ||
+|rougeLsum| 0.1636  | 0.1076  ||