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---
license: apache-2.0
task_categories:
- text-generation
size_categories:
- 10M<n<100M
---
# Dataset Card for Python-Text2Code
- **Repository:** https://github.com/huawei-noah/noah-research/tree/master/NLP/text2code_mrpt
- **Paper [optional]:** https://aclanthology.org/2024.eacl-long.72.pdf
- **Point of Contact:** [Fenia Christopoulou](mailto:[email protected])
## Dataset Description
The data were crawled from existing, public repositories from GitHub before May 2021.
Duplicate files based on the rowKey of each file’s MD5 were removed and 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 then 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 then 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, kept instances with a maximum length of 1024 tokens (docstring+code).
The final dataset contains 23,526,586 text-to-code pairs in Python.
## Data Fields
Each instance contains 3 fields:
- `id`: Unique ID of each pair
- `code`: The python code
- `docstring`: The docstring/problem description
## 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
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**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"
}
## Dataset Card Authors [optional]
Fenia Christopoulou
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