Token Classification
Transformers
Safetensors
English
deberta-v2
named-entity-recognition
biomedical-nlp
gene-recognition
genetics
genomics
molecular-biology
cell-line-name
Instructions to use OpenMed/OpenMed-NER-GenomicDetect-SuperClinical-141M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-GenomicDetect-SuperClinical-141M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomicDetect-SuperClinical-141M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-SuperClinical-141M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-GenomicDetect-SuperClinical-141M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-NER-GenomicDetect-SuperClinical-141M
846ddb9 verified - Xet hash:
- a5c4747ca7c6e357e95d89a504226c3349592d7325027cec92904874a6e4a078
- Size of remote file:
- 283 MB
- SHA256:
- a876486c45f27f721c0f5423e53e62413dbd7bfe7f53a4eb369c6673cecbce29
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