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--- |
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license: apache-2.0 |
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task_categories: |
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- text-generation |
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size_categories: |
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- 10M<n<100M |
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--- |
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# Dataset Card for Python-Text2Code |
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- **Repository:** https://github.com/huawei-noah/noah-research/tree/master/NLP/text2code_mrpt |
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- **Paper [optional]:** https://aclanthology.org/2024.eacl-long.72.pdf |
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- **Point of Contact:** [Fenia Christopoulou](mailto:[email protected]) |
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## Dataset Description |
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The data were crawled from existing, public repositories from GitHub before May 2021. |
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Duplicate files based on the rowKey of each file’s MD5 were removed and files that met the following criteria were kept: |
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(a) the file size is under 1MB; |
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(b) the code is Python3 compatible, using Abstract Syntactic Tree (AST) parsing; |
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(c) there are fewer than 100 characters per line on average; |
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(d) and there are fewer than 1,000 characters in any single line. |
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We then applied AST parsing (via [Tree-sitter](https://tree-sitter.github.io/tree-sitter/)) on the remaining Python files to extract valid functions and |
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their corresponding docstrings. |
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Docstrings were used a "problem descriptions" and were separated from the code. Functions without a docstring were discarded. |
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We then replaced new lines, indentation and dedentation with `<NEW_LINE>`, `<INDENT>` and `<DEDENT>`, respectively, to normalise spaces, which effectively reduced the length |
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of the sequences. |
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Finally, kept instances with a maximum length of 1024 tokens (docstring+code). |
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The final dataset contains 23,526,586 text-to-code pairs in Python. |
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## Data Fields |
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Each instance contains 3 fields: |
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- `id`: Unique ID of each pair |
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- `code`: The python code |
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- `docstring`: The docstring/problem description |
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## Data Splits |
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There is a single data split in the dataset. We randomly sampled 0.1% of the dataset to serve as validation set. |
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## Citation |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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```html |
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@inproceedings{christopoulou-etal-2024-text, |
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title = "Text-to-Code Generation with Modality-relative Pre-training", |
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author = "Christopoulou, Fenia and |
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Zhang, Guchun and |
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Lampouras, Gerasimos", |
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editor = "Graham, Yvette and |
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Purver, Matthew", |
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booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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month = mar, |
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year = "2024", |
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address = "St. Julian{'}s, Malta", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.eacl-long.72", |
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pages = "1194--1208" |
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} |
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## Dataset Card Authors [optional] |
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Fenia Christopoulou |
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