Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
extractive-qa
Languages:
code
Size:
100K - 1M
License:
| annotations_creators: | |
| - expert-generated | |
| language: | |
| - code | |
| language_creators: | |
| - found | |
| license: | |
| - mit | |
| multilinguality: | |
| - monolingual | |
| pretty_name: codequeries | |
| size_categories: | |
| - 100K<n<1M | |
| source_datasets: | |
| - original | |
| tags: | |
| - code | |
| - code question answering | |
| - code semantic parsing | |
| - codeqa | |
| task_categories: | |
| - question-answering | |
| task_ids: | |
| - extractive-qa | |
| # Dataset Card for Codequeries | |
| ## Table of Contents | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [How to use](#how-to-use) | |
| - [Data Splits and Data Fields](#data-splits-and-data-fields) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Additional Information](#additional-information) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| ## Dataset Description | |
| - **Homepage:** [Codequeires](https://huggingface.co/datasets/thepurpleowl/codequeries) | |
| - **Repository:** [Code repo](https://github.com/adityakanade/natural-cubert/) | |
| - **Paper:** | |
| ### Dataset Summary | |
| CodeQueries allows to explore extractive question-answering methodology over code | |
| by providing semantic natural language queries as question and code spans as answer or supporting fact. Given a query, finding the answer/supporting fact spans in code context involves analysis complex concepts and long chains of reasoning. The dataset is provided with five separate settings; details on the setting can be found in the [paper](). | |
| ### Supported Tasks and Leaderboards | |
| Query comprehension for code, Extractive question answering for code. | |
| ### Languages | |
| The dataset contains code context from `python` files. | |
| ## Dataset Structure | |
| ### How to use | |
| The dataset can directly used with huggingface datasets. You can load and iterate through the dataset for the proposed five settings with the following two lines of code: | |
| ```python | |
| import datasets | |
| # instead `twostep`, other settings are <ideal/file_ideal/prefix>. | |
| ds = datasets.load_dataset("thepurpleowl/codequeries", "twostep", split=datasets.Split.TEST) | |
| print(next(iter(ds))) | |
| #OUTPUT: | |
| {'query_name': 'Unused import', | |
| 'code_file_path': 'rcbops/glance-buildpackage/glance/tests/unit/test_db.py', | |
| 'context_block': {'content': '# vim: tabstop=4 shiftwidth=4 softtabstop=4\n\n# Copyright 2010-2011 OpenStack, LLC\ ...', | |
| 'metadata': 'root', | |
| 'header': "['module', '___EOS___']", | |
| 'index': 0}, | |
| 'answer_spans': [{'span': 'from glance.common import context', | |
| 'start_line': 19, | |
| 'start_column': 0, | |
| 'end_line': 19, | |
| 'end_column': 33} | |
| ], | |
| 'supporting_fact_spans': [], | |
| 'example_type': 1, | |
| 'single_hop': False, | |
| 'subtokenized_input_sequence': ['[CLS]_', 'Un', 'used_', 'import_', '[SEP]_', 'module_', '\\u\\u\\uEOS\\u\\u\\u_', '#', ' ', 'vim', ':', ...], | |
| 'label_sequence': [4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...], | |
| 'relevance_label': 1 | |
| } | |
| ``` | |
| ### Data Splits and Data Fields | |
| Detailed information on the data splits for proposed settings can be found in the paper. | |
| In general, data splits in all prpoposed settings have examples in following fields - | |
| ``` | |
| - query_name (query name to uniquely identify the query) | |
| - code_file_path (relative source file path w.r.t. ETH Py150 corpus) | |
| - context_blocks (code blocks as context with metadata) [`prefix` setting doesn't have this field and `twostep` has `context_block`] | |
| - answer_spans (answer spans with metadata) | |
| - supporting_fact_spans (supporting-fact spans with metadata) | |
| - example_type (1(positive)) or 0(negative)) example type) | |
| - single_hop (True or False - for query type) | |
| - subtokenized_input_sequence (example subtokens) [`prefix` setting has the corresponding token ids] | |
| - label_sequence (example subtoken labels) | |
| - relevance_label (0 (not relevant) or 1 (relevant) - relevance label of a block) [only `twostep` setting has this field] | |
| ``` | |
| ## Dataset Creation | |
| The dataset is created by using [ETH Py150 Open corpus](https://github.com/google-research-datasets/eth_py150_open) as source for code contexts. To get natural language queries and corresponding answer/supporting spans in ETH Py150 Open corpus files, CodeQL was used. | |
| ## Additional Information | |
| ### Licensing Information | |
| Codequeries dataset is licensed under the [Apache-2.0](https://opensource.org/licenses/Apache-2.0) License. | |
| ### Citation Information | |
| [More Information Needed] | |