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README.md
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- split: test
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path: data/test-*
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---
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#
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- split: test
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path: data/test-*
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---
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# LCA Project Level Code Completion
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## How to load the dataset
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```
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from datasets import load_dataset
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ds = load_dataset('JetBrains-Research/lca-codegen-small', split='test')
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```
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## Data Point Structure
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* `repo` -- repository name in format `{GitHub_user_name}__{repository_name}`
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* `commit_hash` -- commit hash
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* `completion_file` -- dictionary with the completion file content in the following format:
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* `filename` -- filepath to the completion file
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* `content` -- content of the completion file
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* `completion_lines` -- dictionary where keys are classes of lines and values are a list of integers (numbers of lines to complete). The classes are:
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* `committed` -- line contains at least one function or class that was declared in the committed files
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* `inproject` -- line contains at least one function and class that was declared in the project (excluding previous)
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* `infile` -- line contains at least one function and class that was declared in the completion file (excluding previous)
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* `common` -- line contains at least one function and class that was classified to be common, e.g. `main`, `get`, etc (excluding previous)
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* `non_informative` -- line that was classified to be non-informative, e.g. too short, contains comments, etc
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* `random` -- randomly sampled from the rest of the lines
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* `repo_snapshot` -- dictionary with a snapshot of the repository before the commit. Has the same structure as `completion_file`, but filenames and contents are orginized as lists.
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* `completion_lines_raw` -- the same as `completion_lines`, but before sampling.
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## How we collected the data
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* TBA
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