Instructions to use pucpr/clinicalnerpt-chemical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pucpr/clinicalnerpt-chemical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pucpr/clinicalnerpt-chemical")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pucpr/clinicalnerpt-chemical") model = AutoModelForTokenClassification.from_pretrained("pucpr/clinicalnerpt-chemical") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 517b8ae7d1ee8e6584f0bac6d8b6da44249d886f7c23e0c2aa304f0f3cae6a4f
- Size of remote file:
- 709 MB
- SHA256:
- 7ddeefd997ebe92ee5bba734ee515b31c3a6a9ce99823487074edffa604140c1
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