Instructions to use ckiplab/bert-base-han-chinese-pos-jindai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ckiplab/bert-base-han-chinese-pos-jindai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ckiplab/bert-base-han-chinese-pos-jindai")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ckiplab/bert-base-han-chinese-pos-jindai") model = AutoModelForTokenClassification.from_pretrained("ckiplab/bert-base-han-chinese-pos-jindai", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from ckiplab/bert-base-han-chinese-pos-jindai: direct link, hf CLI and curl.
- Browser
- Download file 535 Bytes
-
https://huggingface.co/ckiplab/bert-base-han-chinese-pos-jindai/resolve/main/tokenizer_config.json
- Command line
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hf download hf://ckiplab/bert-base-han-chinese-pos-jindai/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/ckiplab/bert-base-han-chinese-pos-jindai/resolve/main/tokenizer_config.json
535 Bytes
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "name_or_path": "hub/ckiplab/bert-base-chinese-20210817-001848", "special_tokens_map_file": "/home/cindy666/.cache/huggingface/transformers/d8a1a1b7a3de221ae53bf9d55154b9df9c4cda18409b393ee0fda4bce4ca7818.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "do_basic_tokenize": true, "never_split": null} |