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-125M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-125M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-125M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-125M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-125M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-BloodCancerDetect-SuperMedical-125M
e475687 verified Download test_results.json from OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-125M: direct link, hf CLI and curl.
- Browser
- Download file 181 Bytes
-
https://huggingface.co/OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-125M/resolve/main/test_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-125M/test_results.json
-
curl -L -o test_results.json https://huggingface.co/OpenMed/OpenMed-NER-BloodCancerDetect-SuperMedical-125M/resolve/main/test_results.json
181 Bytes
| { | |
| "eval_accuracy": 0.9274475524475524, | |
| "eval_f1": 0.8488063660477454, | |
| "eval_loss": 0.4817790985107422, | |
| "eval_precision": 1.0, | |
| "eval_recall": 0.7373271889400922 | |
| } |