Instructions to use judithrosell/ClinicalBERT_BioNLP13CG_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use judithrosell/ClinicalBERT_BioNLP13CG_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="judithrosell/ClinicalBERT_BioNLP13CG_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("judithrosell/ClinicalBERT_BioNLP13CG_NER") model = AutoModelForTokenClassification.from_pretrained("judithrosell/ClinicalBERT_BioNLP13CG_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f6b88ec869267d3787f33ad46539301a5a2cfc5a2ae14f2556f300beec8974a
- Size of remote file:
- 539 MB
- SHA256:
- 74a616d298fb91831c0ab4ef9626c440756725d653a447788e200a8b4fdd0964
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