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
- Xet hash:
- 5c42d4d6f29395b8b7143788c8518af577048fe215bb14ead18fb4ade65afd7a
- Size of remote file:
- 5.65 MB
- SHA256:
- 3a1173c2b6e144a02c001e289a05b5dbefddf247c50d4dcf42633158b2968fcb
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