Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Sub-tasks:
document-retrieval
Languages:
code
Size:
100K - 1M
ArXiv:
License:
update readme
Browse files
README.md
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# Dataset Card for
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## Table of Contents
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- [Dataset Card for
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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## Dataset Description
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- **Homepage:**
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- **Paper:**
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- **
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- **Point of Contact:**
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### Dataset Summary
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### Supported Tasks
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[More Information Needed]
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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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annotations_creators: []
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language_creators:
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- found
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languages:
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- Python, Java
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licenses:
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- cc-by-nc-nd-4.0
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multilinguality:
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- multilingual
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pretty_name: RepoBench-Retrieval
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size_categories:
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- unknown
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source_datasets:
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- original
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task_categories:
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- text-retrieval
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task_ids:
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- document-retrieval
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# Dataset Card for RepoBench-R
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## Table of Contents
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- [Dataset Card for RepoBench-R](#dataset-card-for-repobench-r)
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks](#supported-tasks)
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- [Dataset Structure](#dataset-structure)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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## Dataset Description
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- **Homepage:** https://github.com/Leolty/repobench
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- **Paper:** https://arxiv.org/abs/2306.03091
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- **Point of Contact:** til040@ucsd.edu
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### Dataset Summary
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RepoBench-R is a subtask of [RepoBench](https://github.com/Leolty/repobench), targeting the retrieval component of a repository-level auto-completion
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system, focusing on extracting the most relevant code snippet from a project repository for next-line
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code prediction.
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### Supported Tasks
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The dataset supports two programming languages, Python and Java, and contains two settings:
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- `cff`: short for `cross_file_first`, where the cross-file module in next line is first used in the current file.
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- `cfr`: short for `cross_file_random`, where the cross-file module in next line is not first used in the current file.
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For each setting, we provide two levels of difficulty: `easy` and `hard`. Suppose the number of code snippets in the context is $k$, then for the *easy* subset, we have $5 \leq k < 10$, while for the *hard* subset, we have $k \geq 10$.
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So, the specific information of each split is as follows:
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- `python_cff`:
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- `easy`: 8,000 samples
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- `hard`: 4,000 samples
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- `python_cfr`:
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- `easy`: 8,000 samples
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- `hard`: 4,000 samples
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- `java_cff`:
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- `easy`: 8,000 samples
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- `hard`: 4,000 samples
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- `java_cfr`:
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- `easy`: 8,000 samples
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- `hard`: 4,000 samples
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## Dataset Structure
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## Dataset Creation
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