Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
ncbi
pathology
disease
Instructions to use OpenMed/OpenMed-NER-PathologyDetect-BioMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PathologyDetect-BioMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PathologyDetect-BioMed-335M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-BioMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-BioMed-335M") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eval_accuracy": 0.9779791993492534, | |
| "eval_f1": 0.9051724137931034, | |
| "eval_loss": 0.35235390067100525, | |
| "eval_precision": 0.8866995073891626, | |
| "eval_recall": 0.9244314013206163 | |
| } |