Datasets:
Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
language-modeling
Languages:
code
Size:
100K - 1M
ArXiv:
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} | |
} | |
``` |