Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chem
Instructions to use OpenMed/OpenMed-NER-PharmaDetect-PubMed-v2-109M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-PubMed-v2-109M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-PubMed-v2-109M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-PubMed-v2-109M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-PubMed-v2-109M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "eval_accuracy": 0.9894746338676209, | |
| "eval_f1": 0.9548586362556427, | |
| "eval_loss": 0.32125791907310486, | |
| "eval_precision": 0.946203649205415, | |
| "eval_recall": 0.963673420453183 | |
| } |