metadata

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-single-prompt

XLCost for text-to-code synthesis

Dataset Description

This is a subset of XLCoST benchmark, for text-to-code generation at program level for 2 programming languages: Python, C++. This dataset is based on codeparrot/xlcost-text-to-code with the following improvements:

NEWLINE, INDENT and DEDENT were replaced with the corresponding ASCII codes.
the code text has been reformatted using autopep8 for Python and clang-format for cpp.
new columns have been introduced to allow evaluation using pass@k metric.
programs containing more than one function call in the driver code were removed

Languages

The dataset contains text in English and its corresponding code translation. The text contains a set of concatenated code comments that allow to synthesize the program.

Dataset Structure

To load the dataset you need to specify the language(Python or C++).

from datasets import load_dataset
load_dataset("giulio98/xlcost-single-prompt", "Python")
DatasetDict({
    train: Dataset({
        features: ['text', 'context', 'code', 'test', 'output', 'fn_call'],
        num_rows: 8306
    })
    test: Dataset({
        features: ['text', 'context', 'code', 'test', 'output', 'fn_call'],
        num_rows: 812
    })
    validation: Dataset({
        features: ['text', 'context', 'code', 'test', 'output', 'fn_call'],
        num_rows: 427
    })
})

Data Fields

text: natural language description.
context: import libraries/global variables.
code: code at program level.
test: test function call.
output: expected output of the function call.
fn_call: name of the function to call.

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}
}