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# Dataset Summary
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The dataset contains
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# Dataset Collection
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The filtering is performed using zero-shot quality estimation based on [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). The model is prompted to determine which of two files has higher "educational value for learning algorithms in Kotlin". The results of the comparisons are averaged and used to train a binary classifier based on [CodeT5p-220m](https://huggingface.co/Salesforce/codet5p-220m). The binary classifier is then applied to the entire KStack to obtain scores for each sample in the dataset. The log-probability of the classifier prediction used as a criterion of the selection.
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# Opt-out
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If you want your data to be removed from dataset, or have any other questions, please reach out to Sergey Titov sergey.titov@jetbrains.com
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# Dataset Summary
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The dataset contains 25,000 Kotlin code samples selected from the [KStack](https://huggingface.co/datasets/JetBrains/KStack) dataset. The selection is performed based on the value of the code for learning algorithmic concepts in Kotlin. In total, the dataset contains about 23M [CodeLlama-7b](https://huggingface.co/codellama/CodeLlama-7b-hf) tokens (vocab size 32,016).
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# Dataset Collection
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The filtering from [KStack](https://huggingface.co/datasets/JetBrains/KStack) is performed using zero-shot quality estimation based on [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). The model is prompted to determine which of two files has higher "educational value for learning algorithms in Kotlin". The results of the comparisons are averaged and used to train a binary classifier based on [CodeT5p-220m](https://huggingface.co/Salesforce/codet5p-220m). The binary classifier is then applied to the entire KStack to obtain scores for each sample in the dataset. The log-probability of the classifier prediction is used as a criterion of the selection.
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# Opt-out
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If you want your data to be removed from dataset, or have any other questions, please reach out to Sergey Titov: <sergey.titov@jetbrains.com>
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