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- split: train
path: data/Java/train-*
- config_name: JavaScript
data_files:
- split: train
path: data/JavaScript/train-*
- config_name: Julia
data_files:
- split: train
path: data/Julia/train-*
- config_name: Kotlin
data_files:
- split: train
path: data/Kotlin/train-*
- config_name: Less
data_files:
- split: train
path: data/Less/train-*
- config_name: Lua
data_files:
- split: train
path: data/Lua/train-*
- config_name: Mathematica
data_files:
- split: train
path: data/Mathematica/train-*
- config_name: Matlab
data_files:
- split: train
path: data/Matlab/train-*
- config_name: NetLogo
data_files:
- split: train
path: data/NetLogo/train-*
- config_name: NewLisp
data_files:
- split: train
path: data/NewLisp/train-*
- config_name: Nix
data_files:
- split: train
path: data/Nix/train-*
- config_name: OCaml
data_files:
- split: train
path: data/OCaml/train-*
- config_name: Objective-C
data_files:
- split: train
path: data/Objective-C/train-*
- config_name: PHP
data_files:
- split: train
path: data/PHP/train-*
- config_name: Pascal
data_files:
- split: train
path: data/Pascal/train-*
- config_name: Perl
data_files:
- split: train
path: data/Perl/train-*
- config_name: Processing
data_files:
- split: train
path: data/Processing/train-*
- config_name: Prolog
data_files:
- split: train
path: data/Prolog/train-*
- config_name: Python
data_files:
- split: train
path: data/Python/train-*
- config_name: R
data_files:
- split: train
path: data/R/train-*
- config_name: Raku
data_files:
- split: train
path: data/Raku/train-*
- config_name: Ruby
data_files:
- split: train
path: data/Ruby/train-*
- config_name: Rust
data_files:
- split: train
path: data/Rust/train-*
- config_name: SQL
data_files:
- split: train
path: data/SQL/train-*
- config_name: Scala
data_files:
- split: train
path: data/Scala/train-*
- config_name: Scheme
data_files:
- split: train
path: data/Scheme/train-*
- config_name: Scilab
data_files:
- split: train
path: data/Scilab/train-*
- config_name: Starlark
data_files:
- split: train
path: data/Starlark/train-*
- config_name: Swift
data_files:
- split: train
path: data/Swift/train-*
- config_name: Vue
data_files:
- split: train
path: data/Vue/train-*
- config_name: WebAssembly
data_files:
- split: train
path: data/WebAssembly/train-*
---
# The Heap Dataset
We develop **The Heap**, a new contamination-free multilingual code dataset comprising 57 languages, which facilitates LLM evaluation reproducibility. The reproduction packge can be found [here](https://anonymous.4open.science/r/heap-forge/README.md).
# Is your code in The Heap?
If you would like to have your data removed from the dataset, follow the instructions on [GitHub](https://github.com/AISE-TUDelft/The-Heap-opt-out).
# Usage
Using the Datasets API, our dataset can be used as follows:
```python
from datasets import load_dataset
dataset_name = 'redpajama'
language = 'Python'
ds = load_dataset(
"AISE-TUDelft/the-heap",
f"{language}",
split="train",
num_proc=16
)
ds = ds.filter(lambda x: not x[f'exact_duplicates_{dataset_name}'] and not x[f'near_duplicates_{dataset_name}'])
```
# Collection
We collect up to **50,000** public repositories using the [GitHub API](https://docs.github.com/en/rest/search/search?apiVersion=2022-11-28), focusing on *license type*, *star count*, and *creation date*. Repositories with non-permissive licenses are prioritized to reduce contamination, as public code datasets we deduplicate against primarily focus on permissive or no-license repositories. We select repositories created before **August 2024** in decreasing order of their star counts. To handle GitHub rate limits, we use timeouts and pagination during the scraping process.
### Copyleft licenses included in the The Heap
| **License** | **Family** |
| :--------: | :-------: |
| CECILL-1.0, CECILL-1.1, CECILL-2.0,
CECILL-2.1, CECILL-C, EPL-1.0, EPL-2.0,
LGPL-2.1, LGPL-3.0, MS-RL, MPL-2.0 | Weak Copyleft |
| GPL-2.0, GPL-3.0 | Strong Copyleft |
| AGPL-3.0, EUPL-1.1, EUPL-1.2, OSL-3.0 | Network Copyleft |
The features we extract for each repository are illustrated in the example below.
```json
{
"id": 126178683,
"full_name": "halo-dev/halo",
"html_url": "https://github.com/halo-dev/halo",
"stargazers_count": 29115,
"forks_count": 8985,
"watchers_count": 29115,
"open_issues_count": 278,
"language": "Java",
"created_at": "2018-03-21T12:56:52Z",
"pushed_at": "2023-10-28T16:29:39Z",
"license": {
"key": "gpl-3.0",
"name": "GNU General Public License v3.0",
"spdx_id": "GPL-3.0",
"url": "https://api.github.com/licenses/gpl-3.0",
"node_id": "MDc6TGljZW5zZTk="
},
"retrieval_date": "10/30/2023, 3:24:57 PM (Europe/Amsterdam)"
}
```
### Repository Fields
- **id**: unique id of the repo
- **full_name**: complete name of the repo
- **html_url**: URL to the repo
- **stargazers_count**: number of stars of the repo
- **forks_count**: number of forks of the repo
- **watchers_count**: number of watchers of the repo
- **open_issues_count**: number of open issues of the repo at the extraction time
- **language**: main language of the repo
- **created_at**: creation date of the repo
- **pushed_at**: date of the most recent push to the repo until the extraction date
- **license**: license type of the repo
- **retrieval_date**: date when the repo was scraped from GitHub
We start by retrieving repositories with more than **900** stars using **two-month tumbling windows**. If we hit the **1000** repository limit per window (for a personal GitHub account), we shorten the
search space to a **one-month window** and restart the iteration. Otherwise, the window advances by two months. Once the entire timeframe (until **August 2024**) is covered, we reduce the star search space: between **900** and **100** stars, we decrease the interval by **50** (e.g. search between [900, 850]), between **100** and **10** stars, we decrease the interval by **10**, and for the last **10** stars, we decrease by **1**. Since most repositories fall within the **0-100 star range** (e.g. Figure 1 showcases the distribution of repositories with up to **500** stars for Java), using the **creation date** and **star count** filters helps us avoid API limits and scrape more data by narrowing the search space.
The creation date window can be reduced even further (week or day level), in order to extract more data. We remove any potential duplicated repositories obtained due to the pagination process. Lastly, we extract all the files corresponding to each language. We extend the programming languages extension list used for [The Stack](https://gist.github.com/ppisarczyk/43962d06686722d26d176fad46879d41) with 4 languages: EJS, Raku, Starlark, and WebAssembly.
Figure 1: Distribution of scraped repositories with at most 500 stars for Java