Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
extractive-qa
Languages:
code
Size:
100K - 1M
License:
Update readme
Browse files
README.md
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## Dataset Description
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- **Homepage:**
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- **Repository:**
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- **Paper:**
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- **Leaderboard:**
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- **Point of Contact:**
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### Dataset Summary
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### Supported Tasks and Leaderboards
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### Languages
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## Dataset Structure
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### Data Instances
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### Data Fields
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### Data Splits
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## Dataset Creation
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## Dataset Description
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- **Homepage:**
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- **Repository:** [Code repo](https://github.com/adityakanade/natural-cubert/)
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- **Paper:**
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### Dataset Summary
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CodeQueries allows to explore extractive question-answering methodology over code
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by providing semantic queries as question and answer pairs over code context involving
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complex concepts and long chains of reasoning.
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### Supported Tasks and Leaderboards
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### Languages
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The code section have taken from `python` files.
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## Dataset Structure
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### Data Instances
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All splits of all settings have same format. An example looks as follows -
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```
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```
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### Data Fields
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- examples
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- query_name (query name to uniquely identify the query)
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- context_blocks (code blocks supplied as input to the model for prediction)
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- answer_spans (code in answer spans)
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- supporting_fact_spans (code in supporting-fact spans)
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- code_file_path (relative source file path w.r.t. ETH Py150 corpus)
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- example_type (positive(1) or negative(0) example type)
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- subtokenized_input_sequence (example subtokens)
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- label_sequence (example subtoken labels)
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### Data Splits
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| |train |validation |test |
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|ideal | 9427 | 3270| 3245|
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|prefix | - | - | 3245|
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|sliding_window| - | - | 3245|
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|file_ideal | - | - | 3245|
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|twostep | - | - | 3245|
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## Dataset Creation
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