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README.md
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num_examples: 56400
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download_size: 671035436
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dataset_size: 4250555345
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---
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# Dataset Card for "python_codestyles-random-1k"
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-
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num_examples: 56400
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download_size: 671035436
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dataset_size: 4250555345
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license: mit
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tags:
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- python
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- code-style
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- random
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size_categories:
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- 100K<n<1M
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---
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# Dataset Card for "python_codestyles-random-1k"
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This dataset contains negative and positive examples with python code of compliance with a code style. A positive
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example represents compliance with the code style (label is 1). Each example is composed of two components, the first
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component consists of a code that either conforms to the code style or violates it and the second component
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corresponding to an example code that already conforms to a code style. In total, the dataset contains `1.000` completely
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different code styles. The code styles differ in at least one codestyle rule, which is called a `random` codestyle
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dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
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groups. In addition, both groups contain completely different underlying codes.
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The examples contain source code from the following repositories:
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| repository | tag or commit |
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|:-----------------------------------------------------------------------:|:----------------------------------------:|
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| [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python) | f614ed72170011d2d439f7901e1c8daa7deac8c4 |
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| [huggingface/transformers](https://github.com/huggingface/transformers) | v4.31.0 |
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| [huggingface/datasets](https://github.com/huggingface/datasets) | 2.13.1 |
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| [huggingface/diffusers](https://github.com/huggingface/diffusers) | v0.18.2 |
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| [huggingface/accelerate](https://github.com/huggingface/accelerate) | v0.21.0 |
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You can find the corresponding code styles of the examples in the file [additional_data.json](additional_data.json).
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The code styles in the file are split by training and test group and the index corresponds to the class for the
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columns `code_codestyle` and `style_context_codestyle` in the dataset.
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There are 364.400 samples in total and 182.200 positive and 182.200 negative samples.
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