Instructions to use medical-ner-proj/bert-medical-ner-proj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use medical-ner-proj/bert-medical-ner-proj with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="medical-ner-proj/bert-medical-ner-proj")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("medical-ner-proj/bert-medical-ner-proj") model = AutoModelForTokenClassification.from_pretrained("medical-ner-proj/bert-medical-ner-proj", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from medical-ner-proj/bert-medical-ner-proj: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/medical-ner-proj/bert-medical-ner-proj/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://medical-ner-proj/bert-medical-ner-proj@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/medical-ner-proj/bert-medical-ner-proj/resolve/refs%2Fpr%2F1/pytorch_model.bin
436 MB
- Xet hash:
- b4cfd268642d44fc0de730801a1f28889e16ea73abc0297142617a1ded946992
- Size of remote file:
- 436 MB
- SHA256:
- 8c693c3e8ff14400efe207200da40f341d68c7627256a95c94b9d11e0c4dd105
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