
# Dataset Card

This is a dataset of partial solutions to the HumanEval and MBPP code generation benchmarks tranlated into 18+
programming languages. The original benchmark problems were in Python, and we build the dataset as follows:

1. We translate the prompts into a new language using MultiPL-E;
2. We use code-davinci-002 to generate 200 completions for each problem at temperature 0.8;
3. We select a working solution (if one exists) for each problem-language pair.

[This notebook](https://github.com/nuprl/MultiPL-E/blob/main/notebooks/build_synthetic_solutions_dataset.ipynb)
carried out the steps described above.

Note that the dataset does  *not* have solutions for every problem-language pair, since code-davinci-002 cannot
produce a correct solution to every problem.
language:en license:openrail region:us
{"language": ["en"], "license": "openrail", "pretty_name": "MultiPL-E Synthetic Solutions", "dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2185285, "num_examples": 2624}], "download_size": 891673, "dataset_size": 2185285}}
TAGS
#language-English #license-openrail #region-us 


# Dataset Card

This is a dataset of partial solutions to the HumanEval and MBPP code generation benchmarks tranlated into 18+
programming languages. The original benchmark problems were in Python, and we build the dataset as follows:

1. We translate the prompts into a new language using MultiPL-E;
2. We use code-davinci-002 to generate 200 completions for each problem at temperature 0.8;
3. We select a working solution (if one exists) for each problem-language pair.

This notebook
carried out the steps described above.

Note that the dataset does  *not* have solutions for every problem-language pair, since code-davinci-002 cannot
produce a correct solution to every problem.
# Dataset Card

This is a dataset of partial solutions to the HumanEval and MBPP code generation benchmarks tranlated into 18+
programming languages. The original benchmark problems were in Python, and we build the dataset as follows:

1. We translate the prompts into a new language using MultiPL-E;
2. We use code-davinci-002 to generate 200 completions for each problem at temperature 0.8;
3. We select a working solution (if one exists) for each problem-language pair.

This notebook
carried out the steps described above.

Note that the dataset does  *not* have solutions for every problem-language pair, since code-davinci-002 cannot
produce a correct solution to every problem.
TAGS
#language-English #license-openrail #region-us 
 # Dataset Card

This is a dataset of partial solutions to the HumanEval and MBPP code generation benchmarks tranlated into 18+
programming languages. The original benchmark problems were in Python, and we build the dataset as follows:

1. We translate the prompts into a new language using MultiPL-E;
2. We use code-davinci-002 to generate 200 completions for each problem at temperature 0.8;
3. We select a working solution (if one exists) for each problem-language pair.

This notebook
carried out the steps described above.

Note that the dataset does  *not* have solutions for every problem-language pair, since code-davinci-002 cannot
produce a correct solution to every problem.