Upload 45 files

DS_Classification_data/example_input_files/README.md

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+This directory contains example input files for fine-tuning [examples](https://huggingface.co/ctheodoris/Geneformer/tree/main/examples) in the Geneformer model repository. Please note that the fine-tuning examples are meant to be generally applicable and the input datasets and labels will vary dependent on the downstream task. Example input files for a few of the downstream tasks demonstrated in the manuscript are located in this directory, but these only represent a few example fine-tuning applications. 
+
+Please note that GPU resources are required for efficient usage of Geneformer. Additionally, we strongly recommend tuning hyperparameters for each downstream fine-tuning application as this can significantly boost predictive potential in the downstream task (e.g. max learning rate, learning schedule, number of layers to freeze, etc.).

DS_Classification_data/example_input_files/cell_classification/cell_type_annotation/cell_type_test_data.dataset/dataset.arrow

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DS_Classification_data/example_input_files/cell_classification/cell_type_annotation/cell_type_test_data.dataset/dataset_info.json

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DS_Classification_data/example_input_files/cell_classification/cell_type_annotation/cell_type_test_data.dataset/state.json

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+  "_inplace_history": [
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+  "_output_all_columns": false,
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DS_Classification_data/example_input_files/cell_classification/cell_type_annotation/cell_type_train_data.dataset/dataset.arrow

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DS_Classification_data/example_input_files/cell_classification/cell_type_annotation/cell_type_train_data.dataset/dataset_info.json

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+  "dataset_size": null,
+  "description": 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+  "license": 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DS_Classification_data/example_input_files/gene_classification/dosage_sensitive_tfs/README.md

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+The fine-tuning dataset for transcription factor dosage sensitivity application is 10,000 random cells from Genecorpus-30M. Please note that Genecorpus-30M is a large dataset so loading it for the first time may take some time. The Huggingface .dataset format is designed to cache the data though so future usage will be faster in that this cache allows Datasets to avoid re-processing the entire dataset with each use. 

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