Token Classification
Transformers
Safetensors
English
modernbert
named-entity-recognition
biomedical-nlp
gene-recognition
protein-recognition
genomics
molecular-biology
gene/protein
Instructions to use OpenMed/OpenMed-NER-GenomeDetect-ModernMed-395M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-GenomeDetect-ModernMed-395M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomeDetect-ModernMed-395M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-GenomeDetect-ModernMed-395M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-GenomeDetect-ModernMed-395M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 787e5049f70daeadbae69b7ddadb6570f768e5d97bbb56fa0ae3c2cbd06ee7a0
- Size of remote file:
- 792 MB
- SHA256:
- 793219fd0608f8a730bbaedd80aea19cb0254c68b825a37d468481e1d996d948
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