---
dataset_info:
  features:
  - name: repo
    dtype: string
  - name: commit_hash
    dtype: string
  - name: completion_file
    struct:
    - name: filename
      dtype: string
    - name: content
      dtype: string
  - name: completion_lines
    struct:
    - name: infile
      sequence: int32
    - name: inproject
      sequence: int32
    - name: common
      sequence: int32
    - name: commited
      sequence: int32
    - name: non_informative
      sequence: int32
    - name: random
      sequence: int32
  - name: repo_snapshot
    sequence:
    - name: filename
      dtype: string
    - name: content
      dtype: string
  - name: completion_lines_raw
    struct:
    - name: commited
      sequence: int64
    - name: common
      sequence: int64
    - name: infile
      sequence: int64
    - name: inproject
      sequence: int64
    - name: non_informative
      sequence: int64
    - name: other
      sequence: int64
  splits:
  - name: test
    num_bytes: 2972013125
    num_examples: 270
  download_size: 1242136049
  dataset_size: 2972013125
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
---
# LCA Project Level Code Completion
## How to load the dataset
```
from datasets import load_dataset

ds = load_dataset('JetBrains-Research/lca-codegen-large', split='test')
```
## Data Point Structure

* `repo` – repository name in format `{GitHub_user_name}__{repository_name}`
* `commit_hash` – commit hash
* `completion_file` – dictionary with the completion file content in the following format:
* `filename` – filepath to the completion file
* `content` – content of the completion file
* `completion_lines` – dictionary where keys are classes of lines and values are a list of integers (numbers of lines to complete). The classes are:
* `committed` – line contains at least one function or class that was declared in the committed files from `commit_hash`
* `inproject` – line contains at least one function or class that was declared in the project (excluding previous)
* `infile` – line contains at least one function or class that was declared in the completion file (excluding previous)
* `common` – line contains at least one function or class that was classified to be common, e.g., `main`, `get`, etc (excluding previous)
* `non_informative` – line that was classified to be non-informative, e.g. too short, contains comments, etc
* `random` – randomly sampled from the rest of the lines
* `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.
* `completion_lines_raw` – the 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 data point 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.

Large dataset is defined by number of characters in `.py` files from the repository snapshot. This number is from 192K to 768K.

## Dataset Stats

* Number of datapoints: 270
* Number of repositories: 75
* Number of commits: 219

### Completion File
* Number of lines, median: 278
* Number of lines, min: 200
* Number of lines, max: 1694

### Repository Snapshot
* `.py` files: <u>median 84</u>, from 3 to 255
* non `.py` files: <u>median 155</u>, from 8 to 2174
* `.py` lines: <u>median 15466.5</u>
* non `.py` lines: <u>median 18759</u>

### Line Counts:
* infile: 2691
* inproject: 2595
* common: 693
* committed: 1322
* non-informative: 1019
* random: 1311
* **total**: 9631

## Scores
[HF Space](https://huggingface.co/spaces/JetBrains-Research/long-code-arena)