Token Classification
Transformers
Safetensors
English
roberta
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
ncbi
pathology
disease
Instructions to use OpenMed/OpenMed-NER-PathologyDetect-SuperMedical-355M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PathologyDetect-SuperMedical-355M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PathologyDetect-SuperMedical-355M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-SuperMedical-355M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-SuperMedical-355M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-PathologyDetect-SuperMedical-355M
349bc50 verified | { | |
| "eval_accuracy": 0.9734288960755179, | |
| "eval_f1": 0.8759960159362551, | |
| "eval_loss": 0.35658758878707886, | |
| "eval_precision": 0.8690711462450593, | |
| "eval_recall": 0.8830321285140562 | |
| } |