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-PubMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-AnatomyDetect-PubMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-AnatomyDetect-PubMed-335M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-PubMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-AnatomyDetect-PubMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1d42639703f8a2bc024eba9932c862cd8d0ce5b288f063da1b5592702c1d2cc
- Size of remote file:
- 668 MB
- SHA256:
- 12d972c8f7a0c3abafa4e664a53ed72c61793d175463c0b40ccdd7a9c57fdf37
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