Token Classification
Transformers
Safetensors
English
bert
named-entity-recognition
biomedical-nlp
protein-interactions
molecular-biology
biochemistry
systems-biology
protein
protein_complex
protein_enum
protein_familiy_or_group
protein_variant
Instructions to use OpenMed/OpenMed-NER-ProteinDetect-BioMed-109M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-BioMed-109M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-BioMed-109M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-BioMed-109M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-BioMed-109M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3fc2823bdc85bc58ac4aa194238c65ea85b30329439cc75722bfac8c364b0035
- Size of remote file:
- 218 MB
- SHA256:
- 07b5b17905fbc479e0a7be5357bed8e0ea69e5412e744db8dd6cdbfd94421732
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