Instructions to use Shaileshmnnit/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shaileshmnnit/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Shaileshmnnit/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Shaileshmnnit/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("Shaileshmnnit/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Shaileshmnnit/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/Shaileshmnnit/bert-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Shaileshmnnit/bert-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Shaileshmnnit/bert-finetuned-ner/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 291e6f914e26d6230f6d9195f6d4b68186366ccf780a87ae6d96430df194f896
- Size of remote file:
- 431 MB
- SHA256:
- 0387c26e3827ec1ada73e2a5d8c859ee4b932f6dad1fabe99299cde2904b302f
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