Token Classification
Transformers
Safetensors
English
deberta-v2
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-SuperClinical-434M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-SuperClinical-434M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-SuperClinical-434M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-SuperClinical-434M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-SuperClinical-434M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-ProteinDetect-SuperClinical-434M
a5cca1e verified - Xet hash:
- faa8430514b731338df1e946b41488e1373800b40bf678381eddc4a02d2c891d
- Size of remote file:
- 868 MB
- SHA256:
- 9103d57cf47c2fd8256b1fef275772e880ae303276710b0ba615cb236a01b8b3
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