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-PubMed-109M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-DiseaseDetect-PubMed-109M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-DiseaseDetect-PubMed-109M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-PubMed-109M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-PubMed-109M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e27963b09e53daf4a0968f6c6f004db6095431092d08a91f919e6de48d3a8d58
- Size of remote file:
- 218 MB
- SHA256:
- 7bb6e53761696aa828bc930b211b19d64230ae8f0ed3fd6c6be84a93f2095eee
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