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:
- 1d84775670d98e06c632a32ccff018beb485dddd4cce0fffe101542caec6965b
- Size of remote file:
- 15.3 MB
- SHA256:
- 6a5325addfc3b3851d746035d0fb75e3efd3d53e000a05e0ca79cc1df5cb954c
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