Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
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-ZeroShot-NER-Oncology-Multi-209M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M") - Notebooks
- Google Colab
- Kaggle
Download test_results.json from OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M: direct link, hf CLI and curl.
- Browser
- Download file 208 Bytes
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M/resolve/main/test_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M/test_results.json
-
curl -L -o test_results.json https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M/resolve/main/test_results.json
208 Bytes
| { | |
| "eval_loss": 330.18670654296875, | |
| "seqeval_accuracy": 0.9243924392439244, | |
| "seqeval_f1": 0.7497824194952133, | |
| "seqeval_precision": 0.7475054229934924, | |
| "seqeval_recall": 0.7520733304233959 | |
| } |