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--- |
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dataset_info: |
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features: |
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- name: zip |
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dtype: string |
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- name: filename |
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dtype: string |
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- name: contents |
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dtype: string |
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- name: type_annotations |
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sequence: string |
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- name: type_annotation_starts |
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sequence: int64 |
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- name: type_annotation_ends |
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sequence: int64 |
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splits: |
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- name: train |
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num_bytes: 4206116750 |
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num_examples: 548536 |
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download_size: 1334224020 |
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dataset_size: 4206116750 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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license: openrail |
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pretty_name: ManyTypes4Py Reconstruction |
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--- |
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# ManyTypes4Py-Reconstructed |
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This is a reconstruction of the original code from the [ManyTypes4Py paper] |
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from the following paper |
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A. M. Mir, E. Latoškinas and G. Gousios, "ManyTypes4Py: A Benchmark Python |
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Dataset for Machine Learning-based Type Inference," *IEEE/ACM International |
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Conference on Mining Software Repositories (MSR)*, 2021, pp. 585-589 |
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[The artifact] (v0.7) for ManyTypes4Py does not have the original Python files. |
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Instead, each file is pre-processed into a stream of types without comments, |
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and the contents of each repository are stored in a single JSON file. |
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This reconstructed dataset has raw Python code. |
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More specifically: |
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1. We extract the list of repositories from the "clean" subset of ManyTypes4Py, |
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which are the repositories that type-check with *mypy*. |
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2. We attempt to download all repositories, but only succeed in fetching |
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4,663 (out of ~5.2K). |
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3. We augment each file with the text of each type annotation, as well as their |
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start and end positions (in bytes) in the code. |
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## Internal Note |
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The dataset construction code is on the Discovery cluster at `/work/arjunguha-research-group/arjun/projects/ManyTypesForPy_reconstruction`. |
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[ManyTypes4Py paper]: https://arxiv.org/abs/2104.04706 |
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[The artifact]: https://zenodo.org/records/4719447 |