Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
pathology
biocuration
disease
Instructions to use OpenMed/OpenMed-NER-DiseaseDetect-ElectraMed-33M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-DiseaseDetect-ElectraMed-33M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-DiseaseDetect-ElectraMed-33M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-ElectraMed-33M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-ElectraMed-33M") - Notebooks
- Google Colab
- Kaggle
File size: 195 Bytes
d95e2ce | 1 2 3 4 5 6 7 | {
"eval_accuracy": 0.9584024594850453,
"eval_f1": 0.8188268476488475,
"eval_loss": 0.3763342797756195,
"eval_precision": 0.790503744043567,
"eval_recall": 0.8492549593198647
} |