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license: apache-2.0
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license: apache-2.0
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##Overview
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This model uses Cell2Sentence fine-tuning on the Pythia-160m model developed by EleutherAI.
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Cell2Sentence Links:
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GitHub: <https://github.com/vandijklab/cell2sentence-ft>
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Paper: <https://www.biorxiv.org/content/10.1101/2023.09.11.557287v3>
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Pythia Links
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GitHub: <https://github.com/EleutherAI/pythia>
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Paper: <https://arxiv.org/abs/2304.01373>
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Hugging Face: <https://huggingface.co/EleutherAI/pythia-160m>
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##Model Details
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Cell2Sentence is a novel method for adapting large language models to single-cell transcriptomics.
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We transform single-cell RNA sequencing data into sequences of gene names ordered by expression level, termed "cell sentences".
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For more details, we refer to the paper linked above.
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This model is trained on the immune tissue dataset from [Domínguez et al.](https://www.science.org/doi/10.1126/science.abl5197) on the following tasks:
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1. conditional cell generation
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2. unconditional cell generation
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3. cell type prediction
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