Token Classification
Transformers
Safetensors
English
xlm-roberta
named-entity-recognition
biomedical-nlp
cancer-genetics
oncology
gene-regulation
cancer-research
amino_acid
anatomical_system
cancer
cell
cellular_component
developing_anatomical_structure
gene_or_gene_product
immaterial_anatomical_entity
multi-tissue_structure
organ
organism
organism_subdivision
organism_substance
pathological_formation
simple_chemical
tissue
Instructions to use OpenMed/OpenMed-NER-OncologyDetect-ElectraMed-560M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OncologyDetect-ElectraMed-560M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OncologyDetect-ElectraMed-560M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-ElectraMed-560M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-ElectraMed-560M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-OncologyDetect-ElectraMed-560M
46fe4a3 verified - Xet hash:
- 8b03b9e079abc849bdd27d0942fa6a77f9e7836db188512be97e4b3d52f415a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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