Token Classification
Transformers
Safetensors
English
roberta
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-TinyMed-82M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-TinyMed-82M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-TinyMed-82M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-TinyMed-82M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-TinyMed-82M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a08f6d9cc4c2e2ea62cf10bb65849db26685865d9eef245e005ae22fb3724315
- Size of remote file:
- 163 MB
- SHA256:
- e1b011d5bfbe18d2990939d1b94175fcb36b62e3f5f2469eebc601b42ffc425a
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