Token Classification
Transformers
Safetensors
English
distilbert
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-66M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-NER-ProteinDetect-TinyMed-66M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-TinyMed-66M")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-TinyMed-66M") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-NER-ProteinDetect-TinyMed-66M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b05f7d8d6f07567043b9cb6d6ebd47db212964a93e46a2e4163b0116991542f8
- Size of remote file:
- 133 MB
- SHA256:
- c264049addb43434ccf4170b8c78deaae65124214cbeaacb8f534dc360109367
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