Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
code
Multilinguality:
multilingual
Size Categories:
unknown
ArXiv:
Tags:
License:
| annotations_creators: [] | |
| language_creators: | |
| - crowdsourced | |
| - expert-generated | |
| language: | |
| - code | |
| license: | |
| - cc-by-sa-4.0 | |
| multilinguality: | |
| - multilingual | |
| size_categories: | |
| - unknown | |
| source_datasets: [] | |
| task_categories: | |
| - text-generation | |
| task_ids: | |
| - language-modeling | |
| pretty_name: xlcost-text-to-code | |
| # XLCost for text-to-code synthesis | |
| ## Dataset Description | |
| This is a subset of [XLCoST benchmark](https://github.com/reddy-lab-code-research/XLCoST), for text-to-code generation at snippet level and program level for **7** programming languages: `Python, C, C#, C++, Java, Javascript and PHP`. | |
| ## Languages | |
| The dataset contains text in English and its corresponding code translation. Each program is divided into several code snippets, so the snipppet-level subsets contain these code snippets with their corresponding comments, for program-level subsets, the comments were concatenated in one long description. Moreover, programs in all the languages are aligned at the snippet level and the comment for a particular snippet is the same across all the languages. | |
| ## Dataset Structure | |
| To load the dataset you need to specify a subset among the **14 exiting instances**: `LANGUAGE-snippet-level/LANGUAGE-program-level` for `LANGUAGE` in `[Python, C, Csharp, C++, Java, Javascript and PHP]`. By default `Python-snippet-level` is loaded. | |
| ```python | |
| from datasets import load_dataset | |
| load_dataset("codeparrot/xlcost-text-to-code", "Python-program-level") | |
| DatasetDict({ | |
| train: Dataset({ | |
| features: ['text', 'code'], | |
| num_rows: 9263 | |
| }) | |
| test: Dataset({ | |
| features: ['text', 'code'], | |
| num_rows: 887 | |
| }) | |
| validation: Dataset({ | |
| features: ['text', 'code'], | |
| num_rows: 472 | |
| }) | |
| }) | |
| ``` | |
| ```python | |
| next(iter(data["train"])) | |
| {'text': 'Maximum Prefix Sum possible by merging two given arrays | Python3 implementation of the above approach ; Stores the maximum prefix sum of the array A [ ] ; Traverse the array A [ ] ; Stores the maximum prefix sum of the array B [ ] ; Traverse the array B [ ] ; Driver code', | |
| 'code': 'def maxPresum ( a , b ) : NEW_LINE INDENT X = max ( a [ 0 ] , 0 ) NEW_LINE for i in range ( 1 , len ( a ) ) : NEW_LINE INDENT a [ i ] += a [ i - 1 ] NEW_LINE X = max ( X , a [ i ] ) NEW_LINE DEDENT Y = max ( b [ 0 ] , 0 ) NEW_LINE for i in range ( 1 , len ( b ) ) : NEW_LINE INDENT b [ i ] += b [ i - 1 ] NEW_LINE Y = max ( Y , b [ i ] ) NEW_LINE DEDENT return X + Y NEW_LINE DEDENT A = [ 2 , - 1 , 4 , - 5 ] NEW_LINE B = [ 4 , - 3 , 12 , 4 , - 3 ] NEW_LINE print ( maxPresum ( A , B ) ) NEW_LINE'} | |
| ``` | |
| Note that the data undergo some tokenization hence the additional whitespaces and the use of NEW_LINE instead of `\n` and INDENT instead of `\t`, DEDENT to cancel indentation... | |
| ## Data Fields | |
| * text: natural language description/comment | |
| * code: code at snippet/program level | |
| ## Data Splits | |
| Each subset has three splits: train, test and validation. | |
| ## Citation Information | |
| ``` | |
| @misc{zhu2022xlcost, | |
| title = {XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence}, | |
| url = {https://arxiv.org/abs/2206.08474}, | |
| author = {Zhu, Ming and Jain, Aneesh and Suresh, Karthik and Ravindran, Roshan and Tipirneni, Sindhu and Reddy, Chandan K.}, | |
| year = {2022}, | |
| eprint={2206.08474}, | |
| archivePrefix={arXiv} | |
| } | |
| ``` |