Instructions to use jh02/bert_nsmc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jh02/bert_nsmc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jh02/bert_nsmc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jh02/bert_nsmc") model = AutoModelForSequenceClassification.from_pretrained("jh02/bert_nsmc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from jh02/bert_nsmc: direct link, hf CLI and curl.
- Browser
- Download file 537 Bytes
-
https://huggingface.co/jh02/bert_nsmc/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://jh02/bert_nsmc/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/jh02/bert_nsmc/resolve/main/tokenizer_config.json
537 Bytes
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "do_basic_tokenize": true, "never_split": null, "model_max_length": 512, "special_tokens_map_file": "/home/work/.cache/huggingface/transformers/aeaaa3afd086a040be912f92ffe7b5f85008b744624f4517c4216bcc32b51cf0.054ece8d16bd524c8a00f0e8a976c00d5de22a755ffb79e353ee2954d9289e26", "name_or_path": "klue/bert-base", "tokenizer_class": "BertTokenizer"} |