Feature Extraction
Transformers
Safetensors
seqscreen
proteins
molecules
bioinformatics
drug-discovery
custom_code
Instructions to use SaeedLab/BindScreen-Finetuning-LIT_PCBA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaeedLab/BindScreen-Finetuning-LIT_PCBA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SaeedLab/BindScreen-Finetuning-LIT_PCBA", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SaeedLab/BindScreen-Finetuning-LIT_PCBA", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- transformers
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# BindScreen
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This model corresponds to the BinScreen finetuning configuration, in which ESM2 T36 and the projection layers are trained on filtered ChEMBL.
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- transformers
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# BindScreen Finetuning
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This model corresponds to the BinScreen finetuning configuration, in which ESM2 T36 and the projection layers are trained on filtered ChEMBL.
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