Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
chemistry
chem
Instructions to use OpenMed/OpenMed-NER-ChemicalDetect-BioClinical-108M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ChemicalDetect-BioClinical-108M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ChemicalDetect-BioClinical-108M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-BioClinical-108M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-BioClinical-108M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 194 Bytes
8743ede | 1 2 3 4 5 6 7 | {
"eval_accuracy": 0.9838322582155777,
"eval_f1": 0.9424244351630024,
"eval_loss": 0.331601619720459,
"eval_precision": 0.9422845917949153,
"eval_recall": 0.942564320045236
} |