Token Classification
Transformers
Safetensors
English
deberta-v2
named-entity-recognition
biomedical-nlp
protein-recognition
gene-recognition
molecular-biology
genomics
dna
rna
cell_line
cell_type
protein
Instructions to use OpenMed/OpenMed-NER-DNADetect-SuperClinical-141M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-DNADetect-SuperClinical-141M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-DNADetect-SuperClinical-141M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-DNADetect-SuperClinical-141M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-DNADetect-SuperClinical-141M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1498b1f04197b3e43da2d39e213eca885ecbe7a1b822e5d29ebc82e6b78b1662
- Size of remote file:
- 283 MB
- SHA256:
- 70d2879a576df72cca22f28c35f50385a4af62e8c1a85282436d3cd7caf5b002
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