Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
disease-entity-recognition
medical-diagnosis
pathology
biocuration
disease
Instructions to use OpenMed/OpenMed-NER-DiseaseDetect-MultiMed-335M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-DiseaseDetect-MultiMed-335M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-DiseaseDetect-MultiMed-335M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-MultiMed-335M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-MultiMed-335M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed865cafeb7524dd59815533504d920301a9bd324ad0ea38761085721dda972c
- Size of remote file:
- 668 MB
- SHA256:
- 2ce0fa2d6bc3e1beb4185e94ba222cd85ecda62dd79da8d5462fca3a337d0328
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