Token Classification
Transformers
Safetensors
English
xlm-roberta
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
chemistry
chem
Instructions to use OpenMed/OpenMed-NER-ChemicalDetect-MultiMed-568M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ChemicalDetect-MultiMed-568M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ChemicalDetect-MultiMed-568M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-MultiMed-568M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ChemicalDetect-MultiMed-568M") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eval_accuracy": 0.9885337563392105, | |
| "eval_f1": 0.945871669255586, | |
| "eval_loss": 0.3172786831855774, | |
| "eval_precision": 0.9436691356454843, | |
| "eval_recall": 0.9480845083917535 | |
| } |