Token Classification
Transformers
Safetensors
English
modernbert
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-ModernClinical-149M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-ModernClinical-149M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-ModernClinical-149M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-ModernClinical-149M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-ModernClinical-149M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-ProteinDetect-ModernClinical-149M
bfc24be verified - Xet hash:
- a195343616c8cfcf5e17bd0c7fc76db974df4a60d38d9f7366ea6d253d250e38
- Size of remote file:
- 299 MB
- SHA256:
- 389c799e0c32654147d3a9c72033559bc90f54dd894c3c2357704cf440ff3007
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