Token Classification
Transformers
Safetensors
English
roberta
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-SuperMedical-355M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-SuperMedical-355M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-SuperMedical-355M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-SuperMedical-355M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-SuperMedical-355M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-ProteinDetect-SuperMedical-355M
bcef647 verified 
- Xet hash:
- 26884d88e5c82ce8431230e47e3c804c40185c9a1a8edef46b74b425685913ad
- Size of remote file:
- 497 kB
- SHA256:
- 626b37d9b20c44e26c92a8b5bf774107393ae0ad0b482d8e7cb3dc31d960f611
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.