Token Classification
Transformers
Safetensors
English
modernbert
named-entity-recognition
biomedical-nlp
gene-recognition
genetics
genomics
molecular-biology
cell-line-name
Instructions to use OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-GenomicDetect-ModernClinical-395M
7246967 verified Download test_results.json from OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M: direct link, hf CLI and curl.
- Browser
- Download file 196 Bytes
-
https://huggingface.co/OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M/resolve/main/test_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M/test_results.json
-
curl -L -o test_results.json https://huggingface.co/OpenMed/OpenMed-NER-GenomicDetect-ModernClinical-395M/resolve/main/test_results.json
196 Bytes
| { | |
| "eval_accuracy": 0.9982272968370509, | |
| "eval_f1": 0.9846077457795432, | |
| "eval_loss": 0.2956600487232208, | |
| "eval_precision": 0.9889041266674978, | |
| "eval_recall": 0.9803485354097144 | |
| } |