Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
gene-recognition
genetics
genomics
molecular-biology
cell-line-name
Instructions to use OpenMed/OpenMed-NER-GenomicDetect-MultiMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-GenomicDetect-MultiMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomicDetect-MultiMed-335M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-MultiMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-MultiMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 195 Bytes
6fd4711 | 1 2 3 4 5 6 7 | {
"eval_accuracy": 0.99789998299282,
"eval_f1": 0.9863164999693195,
"eval_loss": 0.29738879203796387,
"eval_precision": 0.9831192660550458,
"eval_recall": 0.9895345973898054
} |