Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
Instructions to use DipakBundheliya/ner_bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DipakBundheliya/ner_bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="DipakBundheliya/ner_bert_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("DipakBundheliya/ner_bert_model") model = AutoModelForTokenClassification.from_pretrained("DipakBundheliya/ner_bert_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from DipakBundheliya/ner_bert_model: direct link, hf CLI and curl.
- Browser
- Download file 125 Bytes
-
https://huggingface.co/DipakBundheliya/ner_bert_model/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://DipakBundheliya/ner_bert_model/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/DipakBundheliya/ner_bert_model/resolve/main/special_tokens_map.json
125 Bytes
| { | |
| "cls_token": "[CLS]", | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "unk_token": "[UNK]" | |
| } | |