Instructions to use judithrosell/ClinicalBERT_CRAFT_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use judithrosell/ClinicalBERT_CRAFT_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="judithrosell/ClinicalBERT_CRAFT_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("judithrosell/ClinicalBERT_CRAFT_NER") model = AutoModelForTokenClassification.from_pretrained("judithrosell/ClinicalBERT_CRAFT_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0ae7a75eb69276f4cb1cfe174201f1c8d8a136f92ddf3b65ac6642541b03dc7
- Size of remote file:
- 4.6 kB
- SHA256:
- 4e3ba45b95079a4619851b0dfc61276c5cf833d392c5bda7f5ff56ac30a2bc43
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