Instructions to use rdchambers/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rdchambers/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rdchambers/bert-finetuned-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rdchambers/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("rdchambers/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rdchambers/bert-finetuned-ner: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/rdchambers/bert-finetuned-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rdchambers/bert-finetuned-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rdchambers/bert-finetuned-ner/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 9ddfc39333122f0c3f27378b08bc0d8a1ee1dfaa89e88c428cb415913c3d7346
- Size of remote file:
- 431 MB
- SHA256:
- d7f41e047e312fbda0b5280dd72d10ab0f4c341328229f3c6aec04b867c786e8
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