Instructions to use DeepaPeri/MBERT-TRAINED-CONLL2003-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepaPeri/MBERT-TRAINED-CONLL2003-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DeepaPeri/MBERT-TRAINED-CONLL2003-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DeepaPeri/MBERT-TRAINED-CONLL2003-en") model = AutoModelForTokenClassification.from_pretrained("DeepaPeri/MBERT-TRAINED-CONLL2003-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from DeepaPeri/MBERT-TRAINED-CONLL2003-en: direct link, hf CLI and curl.
- Browser
- Download file 303 Bytes
-
https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2003-en/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://DeepaPeri/MBERT-TRAINED-CONLL2003-en/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/DeepaPeri/MBERT-TRAINED-CONLL2003-en/resolve/main/tokenizer_config.json
303 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, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "distilbert-base-multilingual-cased"} |