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-MultiMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_results.json from OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M: direct link, hf CLI and curl.
- Browser
- Download file 195 Bytes
-
https://huggingface.co/OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M/resolve/main/test_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M/test_results.json
-
curl -L -o test_results.json https://huggingface.co/OpenMed/OpenMed-NER-OncologyDetect-MultiMed-335M/resolve/main/test_results.json
195 Bytes
| { | |
| "eval_accuracy": 0.9293696618151824, | |
| "eval_f1": 0.823077517980975, | |
| "eval_loss": 0.9075292348861694, | |
| "eval_precision": 0.8091231626964014, | |
| "eval_recall": 0.8375216410471644 | |
| } |