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---
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: 111010036
num_examples: 144
download_size: 37603701
dataset_size: 111010036
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-small', 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 organized 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.
Small dataset is defined by number of characters in `.py` files from the repository snapshot. This number is less than 48K.
## Dataset Stats
* Number of datapoints: 144
* Number of repositories: 46
* Number of commits: 63
### Completion File
* Number of lines, median: 310.5
* Number of lines, min: 201
* Number of lines, max: 1916
### Repository Snapshot
* `.py` files: <u>median 4</u>, from 0 to 52
* non `.py` files: <u>median 19.5</u>, from 1 to 1044
* `.py` lines: <u>median 128</u>
* non `.py` lines: <u>median 1227</u>
### Line Counts:
* infile: 1430
* inproject: 95
* common: 500
* committed: 1426
* non-informative: 532
* random: 703
* **total**: 4686
## Scores
[HF Space](https://huggingface.co/spaces/JetBrains-Research/long-code-arena)
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