Token Classification
Transformers
Safetensors
English
roberta
named-entity-recognition
biomedical-nlp
species-recognition
taxonomy
organism-identification
biodiversity
species
Instructions to use OpenMed/OpenMed-NER-OrganismDetect-SuperMedical-355M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OrganismDetect-SuperMedical-355M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OrganismDetect-SuperMedical-355M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OrganismDetect-SuperMedical-355M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OrganismDetect-SuperMedical-355M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-OrganismDetect-SuperMedical-355M
61370fe verified - Xet hash:
- ad9c208f312e10a931463a316c11c9c6f7757f31c55ec0fd28306f3cc46dfe7a
- Size of remote file:
- 709 MB
- SHA256:
- 0b91181b0bdf86a233a5cf6d12f0b422846bd4e401dbaa04c7a6de6ae25150f3
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