Token Classification
Transformers
Safetensors
English
bert
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-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OncologyDetect-ElectraMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OncologyDetect-ElectraMed-335M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-ElectraMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-ElectraMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-OncologyDetect-ElectraMed-335M
2420236 verified | { | |
| "eval_accuracy": 0.9127398025703111, | |
| "eval_f1": 0.7736546766310671, | |
| "eval_loss": 0.9799578785896301, | |
| "eval_precision": 0.7628250892401836, | |
| "eval_recall": 0.7847961806830701 | |
| } |