Instructions to use sumitrsch/Indic-bert_multiconer22_bn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumitrsch/Indic-bert_multiconer22_bn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sumitrsch/Indic-bert_multiconer22_bn")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sumitrsch/Indic-bert_multiconer22_bn") model = AutoModelForTokenClassification.from_pretrained("sumitrsch/Indic-bert_multiconer22_bn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sumitrsch/Indic-bert_multiconer22_bn: direct link, hf CLI and curl.
- Browser
- Download file 131 MB
-
https://huggingface.co/sumitrsch/Indic-bert_multiconer22_bn/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sumitrsch/Indic-bert_multiconer22_bn/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sumitrsch/Indic-bert_multiconer22_bn/resolve/main/pytorch_model.bin
131 MB
- Xet hash:
- 9f6f3e65bd7b59b26b5f669716c6e2f3502bb025f84372e4baaf84ea3f67e40f
- Size of remote file:
- 131 MB
- SHA256:
- 9469f1bfe15a348456ac5057653634b3c365f7ab10e69e8f34c1b42a8e0ec1f3
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