Token Classification
Transformers
Safetensors
English
modernbert
named-entity-recognition
biomedical-nlp
species-recognition
taxonomy
organism-identification
biodiversity
species
Instructions to use OpenMed/OpenMed-NER-OrganismDetect-ModernClinical-149M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OrganismDetect-ModernClinical-149M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OrganismDetect-ModernClinical-149M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OrganismDetect-ModernClinical-149M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OrganismDetect-ModernClinical-149M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-OrganismDetect-ModernClinical-149M
0f18c2d verified - Xet hash:
- c7b85ef875bf1c57d3dc26995521c97a89ebf7aeccee4c71bbf841efa603bcfa
- Size of remote file:
- 299 MB
- SHA256:
- ffd9056b9581757b8f5449a88256a5ea764ee83a2777b8b9604540a54ca0c87e
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