Token Classification
Transformers
Safetensors
English
roberta
named-entity-recognition
biomedical-nlp
leukemia
hematology
cancer
clinical-medicine
cl
Instructions to use OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-355M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-355M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-355M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-355M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-355M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-BloodCancerDetect-SuperMedical-355M
9b9ee2a verified | { | |
| "eval_accuracy": 0.9248251748251748, | |
| "eval_f1": 0.8421052631578947, | |
| "eval_loss": 0.48452290892601013, | |
| "eval_precision": 0.9815950920245399, | |
| "eval_recall": 0.7373271889400922 | |
| } |