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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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- [More Information Needed]
 
 
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  ### Supported Tasks and Leaderboards
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  ### Languages
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- [More Information Needed]
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  ## Dataset Structure
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  ### Data Instances
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- [More Information Needed]
 
 
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  ### Data Fields
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- [More Information Needed]
 
 
 
 
 
 
 
 
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  ### Data Splits
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- [More Information Needed]
 
 
 
 
 
 
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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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+ |--------------|:----:|:---------:|:---:|
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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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