Token Classification
Transformers
Safetensors
English
albert
ner
named-entity-recognition
indic-languages
bert
medical-nlp
regulatory
pharmaceutical
Instructions to use sharkdodo/Indic-Bert-NER-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharkdodo/Indic-Bert-NER-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sharkdodo/Indic-Bert-NER-Model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sharkdodo/Indic-Bert-NER-Model") model = AutoModelForTokenClassification.from_pretrained("sharkdodo/Indic-Bert-NER-Model") - Notebooks
- Google Colab
- Kaggle
| { | |
| "bos_token": "[CLS]", | |
| "cls_token": "[CLS]", | |
| "eos_token": "[SEP]", | |
| "mask_token": { | |
| "content": "[MASK]", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": "<pad>", | |
| "sep_token": "[SEP]", | |
| "unk_token": "<unk>" | |
| } | |