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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-large', 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 from `commit_hash`
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* `inproject` – line contains at least one function or class that was declared in the project (excluding previous)
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* `infile` – line contains at least one function or class that was declared in the completion file (excluding previous)
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* `common` – line contains at least one function or 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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To collect the data, we cloned repositories from GitHub where the main language is Python.
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The completion file for each data point is a `.py` file that was added to the repository in a commit.
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The state of the repository before this commit is the repo snapshot.
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Large dataset is defined by number of characters in `.py` files from the repository snapshot. This number is from 192K to 768K.
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## Dataset Stats
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* Number of datapoints: 270
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* Number of repositories: 75
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* Number of commits: 219
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### Completion File
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* Number of lines, median: 278
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* Number of lines, min: 200
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* Number of lines, max: 1694
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### Repository Snapshot
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* `.py` files: <u>median 84</u>, from 3 to 255
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* non `.py` files: <u>median 155</u>, from 8 to 2174
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* `.py` lines: <u>median 15466.5</u>
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* non `.py` lines: <u>median 18759</u>
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### Line Counts:
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* infile: 2691
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* inproject: 2595
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* common: 693
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* committed: 1322
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* non-informative: 1019
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* random: 1311
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* **total**: 9631
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## Scores
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[HF Space](https://huggingface.co/spaces/JetBrains-Research/long-code-arena)
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