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