Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
anatomical-entity-recognition
medical-terminology
anatomy
healthcare
Instructions to use OpenMed/OpenMed-NER-AnatomyDetect-MultiMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-AnatomyDetect-MultiMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-AnatomyDetect-MultiMed-335M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-MultiMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-MultiMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 22bc8c5a3215ec604315af28d8ec6442c6646afe1e765a77ff7dfd0efb418bf2
- Size of remote file:
- 499 kB
- SHA256:
- a7298a0251fd45794a058505437aac1f13de3325cdfabaf6cb7520b9c7ad1ea5
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