Token Classification
Transformers
Safetensors
English
distilbert
named-entity-recognition
biomedical-nlp
chemical-entity-recognition
drug-discovery
pharmacology
biocuration
chem
Instructions to use OpenMed/OpenMed-NER-PharmaDetect-TinyMed-65M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-PharmaDetect-TinyMed-65M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-TinyMed-65M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-TinyMed-65M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-PharmaDetect-TinyMed-65M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 195 Bytes
54ff721 | 1 2 3 4 5 6 7 | {
"eval_accuracy": 0.9829628154253933,
"eval_f1": 0.9465826789133391,
"eval_loss": 0.3313327133655548,
"eval_precision": 0.943884892086331,
"eval_recall": 0.9492959314298436
} |