Token Classification
Transformers
Safetensors
English
roberta
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chem
Instructions to use OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-355M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-355M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-355M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-355M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-SuperMedical-355M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-PharmaDetect-SuperMedical-355M
e3c1856 verified | { | |
| "eval_accuracy": 0.9881403309030135, | |
| "eval_f1": 0.9584678695789807, | |
| "eval_loss": 0.32379981875419617, | |
| "eval_precision": 0.9519587499213985, | |
| "eval_recall": 0.9650666156690253 | |
| } |