Instructions to use JunHyung1206/kote_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunHyung1206/kote_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JunHyung1206/kote_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JunHyung1206/kote_classification") model = AutoModelForSequenceClassification.from_pretrained("JunHyung1206/kote_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from JunHyung1206/kote_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.8 kB
-
https://huggingface.co/JunHyung1206/kote_classification/resolve/main/config.json
- Command line
-
hf download hf://JunHyung1206/kote_classification/config.json
-
curl -L -o config.json https://huggingface.co/JunHyung1206/kote_classification/resolve/main/config.json
1.8 kB
| { | |
| "_name_or_path": "./models/train_dataset/", | |
| "architectures": [ | |
| "RobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 0, | |
| "classifier_dropout": null, | |
| "eos_token_id": 2, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "\uae30\uc068(\ud589\ubcf5\ud55c)", | |
| "1": "\uace0\ub9c8\uc6b4", | |
| "2": "\uc124\ub808\ub294(\uae30\ub300\ud558\ub294)", | |
| "3": "\uc0ac\ub791\ud558\ub294", | |
| "4": "\uc990\uac70\uc6b4(\uc2e0\ub098\ub294)", | |
| "5": "\uc77c\uc0c1\uc801\uc778", | |
| "6": "\uc0dd\uac01\uc774 \ub9ce\uc740", | |
| "7": "\uc2ac\ud514(\uc6b0\uc6b8\ud55c)", | |
| "8": "\ud798\ub4e6(\uc9c0\uce68)", | |
| "9": "\uc9dc\uc99d\ub0a8", | |
| "10": "\uac71\uc815\uc2a4\ub7ec\uc6b4(\ubd88\uc548\ud55c)" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "\uac71\uc815\uc2a4\ub7ec\uc6b4(\ubd88\uc548\ud55c)": 10, | |
| "\uace0\ub9c8\uc6b4": 1, | |
| "\uae30\uc068(\ud589\ubcf5\ud55c)": 0, | |
| "\uc0ac\ub791\ud558\ub294": 3, | |
| "\uc0dd\uac01\uc774 \ub9ce\uc740": 6, | |
| "\uc124\ub808\ub294(\uae30\ub300\ud558\ub294)": 2, | |
| "\uc2ac\ud514(\uc6b0\uc6b8\ud55c)": 7, | |
| "\uc77c\uc0c1\uc801\uc778": 5, | |
| "\uc990\uac70\uc6b4(\uc2e0\ub098\ub294)": 4, | |
| "\uc9dc\uc99d\ub0a8": 9, | |
| "\ud798\ub4e6(\uc9c0\uce68)": 8 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "tokenizer_class": "BertTokenizer", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.25.1", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 32000 | |
| } | |