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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


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")
    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

     title = {XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence},
     url = {},
     author = {Zhu, Ming and Jain, Aneesh and Suresh, Karthik and Ravindran, Roshan and Tipirneni, Sindhu and Reddy, Chandan K.},
     year = {2022},
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