Token Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use Francesco-A/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Francesco-A/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Francesco-A/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Francesco-A/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Francesco-A/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Francesco-A/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 315 Bytes
-
https://huggingface.co/Francesco-A/bert-finetuned-ner/resolve/b0468c2472fccfd9b2232bc568b38c0e5ba36ece/tokenizer_config.json
- Command line
-
hf download hf://Francesco-A/bert-finetuned-ner@b0468c2472fccfd9b2232bc568b38c0e5ba36ece/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Francesco-A/bert-finetuned-ner/resolve/b0468c2472fccfd9b2232bc568b38c0e5ba36ece/tokenizer_config.json
315 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |