Token Classification
Transformers
Safetensors
English
xlm-roberta
named-entity-recognition
biomedical-nlp
anatomical-entity-recognition
medical-terminology
anatomy
healthcare
Instructions to use OpenMed/OpenMed-NER-AnatomyDetect-BigMed-278M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-AnatomyDetect-BigMed-278M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-AnatomyDetect-BigMed-278M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-BigMed-278M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-BigMed-278M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 703af46b937d038c6b578402f23381cc8024ef800be60b99b5dc4245f88e3107
- Size of remote file:
- 555 MB
- SHA256:
- bb12b399da5542eb58e5ce0d51a3864c78bd82fd0246bef4a8ccfbad69442b3a
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