Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
species-recognition
taxonomy
organism-identification
biodiversity
species
Instructions to use OpenMed/OpenMed-NER-OrganismDetect-ElectraMed-33M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-OrganismDetect-ElectraMed-33M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-OrganismDetect-ElectraMed-33M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-OrganismDetect-ElectraMed-33M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-OrganismDetect-ElectraMed-33M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-OrganismDetect-ElectraMed-33M
f6cd5af verified - Xet hash:
- 80175a33c03775109c984780d339fa548ec979394383523fd7a410485aad9a80
- Size of remote file:
- 66.4 MB
- SHA256:
- 1111f4e700f31f660eb4c46989e8d0b58da298ad92b8a348a1ae6fa1059e1fb6
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