Datasets:
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueTcTextToCode
tags:
- text-to-code
dataset_info:
features:
- name: id
dtype: int32
- name: nl
dtype: string
- name: code
dtype: string
splits:
- name: train
num_bytes: 96225531
num_examples: 100000
- name: validation
num_bytes: 1749743
num_examples: 2000
- name: test
num_bytes: 1609298
num_examples: 2000
download_size: 34258354
dataset_size: 99584572
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
Dataset Card for "code_x_glue_tc_text_to_code"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
Dataset Summary
CodeXGLUE text-to-code dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/text-to-code
The dataset we use is crawled and filtered from Microsoft Documentation, whose document located at https://github.com/MicrosoftDocs/.
Supported Tasks and Leaderboards
machine-translation
: The dataset can be used to train a model for generating Java code from an English natural language description.
Languages
- Java programming language
Dataset Structure
Data Instances
An example of 'train' looks as follows.
{
"code": "boolean function ( ) { return isParsed ; }",
"id": 0,
"nl": "check if details are parsed . concode_field_sep Container parent concode_elem_sep boolean isParsed concode_elem_sep long offset concode_elem_sep long contentStartPosition concode_elem_sep ByteBuffer deadBytes concode_elem_sep boolean isRead concode_elem_sep long memMapSize concode_elem_sep Logger LOG concode_elem_sep byte[] userType concode_elem_sep String type concode_elem_sep ByteBuffer content concode_elem_sep FileChannel fileChannel concode_field_sep Container getParent concode_elem_sep byte[] getUserType concode_elem_sep void readContent concode_elem_sep long getOffset concode_elem_sep long getContentSize concode_elem_sep void getContent concode_elem_sep void setDeadBytes concode_elem_sep void parse concode_elem_sep void getHeader concode_elem_sep long getSize concode_elem_sep void parseDetails concode_elem_sep String getType concode_elem_sep void _parseDetails concode_elem_sep String getPath concode_elem_sep boolean verify concode_elem_sep void setParent concode_elem_sep void getBox concode_elem_sep boolean isSmallBox"
}
Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
default
field name | type | description |
---|---|---|
id | int32 | Index of the sample |
nl | string | The natural language description of the task |
code | string | The programming source code for the task |
Data Splits
name | train | validation | test |
---|---|---|---|
default | 100000 | 2000 | 2000 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
https://github.com/microsoft, https://github.com/madlag
Licensing Information
Computational Use of Data Agreement (C-UDA) License.
Citation Information
@article{iyer2018mapping,
title={Mapping language to code in programmatic context},
author={Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:1808.09588},
year={2018}
}
Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset.