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monsoon-nlp
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README.md
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# Protein Pairs and Similarity
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Selected protein similarities within training, test, and validation sets.
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Each protein gets two similarities selected at random and (
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The protein is represented by its UniProt ID and its amino acid sequence (using IUPAC-IUB codes where each amino acid maps to a letter of the alphabet, see: https://en.wikipedia.org/wiki/FASTA_format ).
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The distance column is cosine distance (identical = 0) on UniProt / SwissProt 1,024-dimension embeddings, downloaded in March 2024.
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For models and training scripts optimizing for similarity, you should use `(1 - distance)`.
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Train / test / validation splits are from [khairi/uniprot-swissprot](https://huggingface.co/datasets/khairi/uniprot-swissprot/tree/39d2f9010ceb2599a12397b79babc902f8c440e6)
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# Protein Pairs and Similarity
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Selected protein similarities within training, test, and validation sets.
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Each protein gets two similarities selected at random and (usually) proteins within the top and bottom quintiles for similarity.
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The protein is represented by its UniProt ID and its amino acid sequence (using IUPAC-IUB codes where each amino acid maps to a letter of the alphabet, see: https://en.wikipedia.org/wiki/FASTA_format ).
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The distance column is cosine distance (identical = 0) on UniProt / SwissProt 1,024-dimension embeddings, downloaded in March 2024, based on the https://huggingface.co/Rostlab/prot_t5_xl_uniref50 model.
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For models and training scripts optimizing for similarity, you should use `(1 - distance)`.
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Train / test / validation splits are from [khairi/uniprot-swissprot](https://huggingface.co/datasets/khairi/uniprot-swissprot/tree/39d2f9010ceb2599a12397b79babc902f8c440e6)
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