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---
license: apache-2.0
dataset_info:
- config_name: default
features:
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- name: commit_hash
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path: data/huge_context/test-*
- config_name: large_context
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path: data/large_context/test-*
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---
# 🏟️ Long Code Arena (Project-level code completion)
This is the benchmark for Project-level code completion task as part of the [🏟️ Long Code Arena benchmark](https://huggingface.co/spaces/JetBrains-Research/long-code-arena).
Each datapoint contains the file for completion, a list of lines to complete with their categories (see the categorization below), and a repository snapshot that can be used to build the context.
All the repositories are published under permissive licenses (MIT, Apache-2.0, BSD-3-Clause, and BSD-2-Clause). The datapoints can be removed upon request.
## How-to
Load the data via [load_dataset](https://huggingface.co/docs/datasets/v2.14.3/en/package_reference/loading_methods#datasets.load_dataset):
```
from datasets import load_dataset
config_names = [
'small_context',
'medium_context',
'large_context',
'huge_context'
]
ds = load_dataset('JetBrains-Research/lca-project-level-code-completion', config_name, split='test')
```
## Dataset Structure
Datapoints in the dataset have the following structure:
* `repo` – repository name in the format `{GitHub_user_name}__{repository_name}`
* `commit_hash` – commit hash of the repository
* `completion_file` – dictionary with the completion file content in the following format:
* `filename` – path to the completion file
* `content` – content of the completion file
* `completion_lines` – dictionary where the keys are categories of lines and values are a list of integers (numbers of lines to complete). The categories are:
* `committed` – line contains at least one function or class from the files that were added on the completion file commit
* `inproject` – line contains at least one function or class from the repository snapshot at the moment of completion
* `infile` – line contains at least one function or class from the completion file
* `common` – line contains at least one function or class with common names, e.g., `main`, `get`, etc.
* `non_informative` – line that was classified to be non-informative, e.g., too short, contains comments, etc.
* `random` – other lines.
* `repo_snapshot` – dictionary with a snapshot of the repository before the commit. It has the same structure as `completion_file`, but filenames and contents are orginized as lists.
* `completion_lines_raw` – same as `completion_lines`, but before sampling
## How we collected the data
To collect the data, we cloned repositories from GitHub where the main language is Python.
The completion file for each datapoint is a `.py` file that was added to the repository in a commit.
The state of the repository before this commit is the repo snapshot.
The dataset configurations are based on the number of characters in `.py` files from the repository snapshot:
- `small_context` – less than 48K characters;
- `medium_context` – from 48K to 192K characters;
- `large_context` – from 192K to 768K characters;
- `huge_context` – more than 768K characters.
## Datasets Stats
| Dataset | Number of datapoints | Number of repositories | Number of commits |
|:-----------------:|:--------------------:|:----------------------:|:-----------------:|
| `small_context` | 144 | 46 | 63 |
| `medium_context` | 224 | 80 | 175 |
| `large_context` | 270 | 75 | 219 |
| `huge_context` | 296 | 75 | 252 |
### Completion File
| Dataset | Completion file lines, min | Completion file lines, max | Completion file lines, median |
|:------------------:|:--------------------------:|:--------------------------:|:------------------------------:|
| `small_context` | 201 | 1916 | 310.5 |
| `medium_context` | 200 | 1648 | 310.0 |
| `large_context` | 200 | 1694 | 278.0 |
| `huge_context` | 200 | 1877 | 313.5 |
### Repository Snapshot `.py` files
| Dataset | Context py files number, min | Context py files number, max | Context py files number, median | Context py lines, median |
|:------------------:|:----------------------------:|:----------------------------:|:--------------------------------:|:-------------------------:|
| `small_context` | 0 | 52 | 4.0 | 128.0 |
| `medium_context` | 3 | 117 | 34.0 | 3786.0 |
| `large_context` | 3 | 255 | 84.0 | 15466.5 |
| `huge_context` | 47 | 5227 | 261.0 | 49811.0 |
### Repository Snapshot non `.py` files
| Dataset | Context non-py files number, min | Context non-py files number, max | Context non-py files number, median | Context non-py lines, median |
|:------------------:|:---------------------------------:|:---------------------------------:|:-----------------------------------:|:-----------------------------:|
| `small_context` | 1 | 1044 | 19.5 | 1227.0 |
| `medium_context` | 3 | 3977 | 64.5 | 9735.0 |
| `large_context` | 8 | 2174 | 155.0 | 18759.0 |
| `huge_context` | 24 | 7687 | 262.0 | 60163.0 |
### Line Counts:
| Dataset | *infile* | *inproject* | *common* | *commited* | *non-informative* | *random* | **all** |
|:------------------:|:--------:|:-----------:|:--------:|:----------:|:-----------------:|:--------:|:-----:|
| `small_context` | 1430 | 95 | 500 | 1426 | 532 | 703 | **4686** |
| `medium_context` | 2224 | 2236 | 779 | 1495 | 858 | 1084 | **8676** |
| `large_context` | 2691 | 2595 | 693 | 1322 | 1019 | 1311 | **9631** |
| `huge_context` | 2608 | 2901 | 692 | 1019 | 1164 | 1426 | **9810** |
## Scores
You can find the results of running various models on this dataset in our [leaderboard](https://huggingface.co/spaces/JetBrains-Research/long-code-arena).
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