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-BioMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OncologyDetect-BioMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OncologyDetect-BioMed-335M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-BioMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-BioMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eval_accuracy": 0.9516292868544525, | |
| "eval_f1": 0.8659537973066677, | |
| "eval_loss": 0.8314083218574524, | |
| "eval_precision": 0.8539552292521414, | |
| "eval_recall": 0.8782943440923897 | |
| } |