Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
protein-interactions
molecular-biology
biochemistry
systems-biology
protein
protein_complex
protein_enum
protein_familiy_or_group
protein_variant
Instructions to use OpenMed/OpenMed-NER-ProteinDetect-BioClinical-108M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-BioClinical-108M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-BioClinical-108M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-BioClinical-108M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-BioClinical-108M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-ProteinDetect-BioClinical-108M
ce6693a verified - Xet hash:
- f37f48d44e8ff2d93c6c2a0df67b3918425bcb43f1db592f61982744ce42151e
- Size of remote file:
- 215 MB
- SHA256:
- 7b7e3d569a9930918633d07933f3811854110536bd0e5e024b2229cd468a242a
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