# 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 program level for **2** programming languages: `Python, C++`. This dataset is based on [codeparrot/xlcost-text-to-code](https://huggingface.co/datasets/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++). 
```python
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}
}
```
task_categories:text-generation task_ids:language-modeling language_creators:crowdsourced language_creators:expert-generated multilinguality:multilingual size_categories:unknown language:code license:cc-by-sa-4.0 arxiv:2206.08474 region:us
{"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"}
TAGS
#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #language-code #license-cc-by-sa-4.0 #arxiv-2206.08474 #region-us 

# 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++). 

## 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.


# 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++). ## 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.
TAGS
#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #language-code #license-cc-by-sa-4.0 #arxiv-2206.08474 #region-us 
 # 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++). ## 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.
passage: TAGS
#task_categories-text-generation #task_ids-language-modeling #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #language-code #license-cc-by-sa-4.0 #arxiv-2206.08474 #region-us 
# 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++).## 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.