Text Classification
Transformers
PyTorch
bert
protein language model
biology
text-embeddings-inference
Instructions to use GleghornLab/SYNTERACT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GleghornLab/SYNTERACT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GleghornLab/SYNTERACT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GleghornLab/SYNTERACT") model = AutoModelForSequenceClassification.from_pretrained("GleghornLab/SYNTERACT") - Notebooks
- Google Colab
- Kaggle
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license: cc-by-nc-
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library_name: transformers
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datasets:
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- BIOGRID
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K V C E F M I S Q L G L Q K K N I K I H G F
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example_title: Interacting proteins
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<img src="https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/Ro4uhQDurP-x7IHJj11xa.png" width="350">
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## Model description
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license: cc-by-nc-4.0
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library_name: transformers
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datasets:
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- BIOGRID
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K V C E F M I S Q L G L Q K K N I K I H G F
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example_title: Interacting proteins
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# [SYNTERACT 2.0](https://huggingface.co/Synthyra/SYNTERACT2) is coming soon, please stay tuned!
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<img src="https://cdn-uploads.huggingface.co/production/uploads/62f2bd3bdb7cbd214b658c48/Ro4uhQDurP-x7IHJj11xa.png" width="350">
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## Model description
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