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 tokenizer_config.json from JunHyung1206/kote_classification: direct link, hf CLI and curl.
- Browser
- Download file 607 Bytes
-
https://huggingface.co/JunHyung1206/kote_classification/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://JunHyung1206/kote_classification/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/JunHyung1206/kote_classification/resolve/main/tokenizer_config.json
607 Bytes
| { | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": false, | |
| "eos_token": "[SEP]", | |
| "mask_token": "[MASK]", | |
| "max_length": 512, | |
| "model_max_length": 512, | |
| "name_or_path": "./models/train_dataset/", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/opt/ml/.cache/huggingface/hub/models--klue--roberta-large/snapshots/5193b95701189160c45d02a1033a4ea55bdbe259/special_tokens_map.json", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |