DISCLAIMER

This represents only a subset of the final dataset (taking into account the new HuggingFace LFS storage limits). The complete dataset will be released following the camera-ready submission of our paper.

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.

Is your code in The Heap?

An opt-out mechanism will be provided for the final release of the dataset.

Collection

We collect up to 50,000 public repositories using the GitHub API, 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.

  {
    "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 with 4 languages: EJS, Raku, Starlark, and WebAssembly.

Figure 1: Distribution of scraped repositories with at most 500 stars for Java

Cleaning

The next stage in our dataset pipeline is the cleaning procedure. We exclude any files larger than 10 MB and those with fewer than 10 words.

Deduplication

The final stage of our dataset pipeline is the deduplication process. We apply both exact and near deduplication against open code datasets listed in the table below.

Open code datasets used for deduplication

Dataset	Size
The Stack V2	67.5 TB
The Stack	6.4 TB
Red Pajama	2.67 TB
GitHub Code	1 TB
CodeParrot	180 GB

Exact Deduplication

We remove exact duplicates within our dataset itself, and then we apply exact deduplication against the open datasets. For that, we use the sha256 function to generate hashes for each file. We choose this hash function because it provides a uniform distribution of hash values, minimizes collisions, and ensures even distribution across the hash space.

Near Deduplication

We apply the MinHashLSH algorithm using the datasketch1 library. To calculate the minhashes, we use the same hash function as above, but we extract the first 16 bytes to generate 128-bit hash values. This approach balances the need for a strong hash function with the efficiency of a shorter hash length.

Additionally, we use 128 file permutations for LSH, with weights of 0.4 for precision and 0.6 for recall. We generate 7-character shingles after lowercasing the file content and removing whitespace. We find that 7-shingles provide a reasonable trade-off between the number of shingles and the data processed, being small enough to keep the number of unique shingles manageable yet large enough to provide meaningful comparisons. It was shown that the number of shingles should be large enough to ensure a low probability of shingles appearing across documents, with k = 5 suggested for smaller documents such as emails. However, code files usually contain a larger dictionary of characters than emails, including arithmetic and comparison operators which are less frequent in emails. Thus, given the increased complexity and size of code files, we consider 7-shingles to be appropriate to capture sufficient context, ensuring uniqueness and reducing false positives, which smaller shingles such as k = 5 might fail to achieve. Furthermore, k = 9 was shown to be a safe choice for large research articles, however, for our needs, 7-shingles strike a balance between accuracy and computational efficiency, crucial for handling the extensive size of the datasets. This choice provides better computational efficiency by reducing the number of comparisons while maintaining a manageable shingle space. Lastly, we use a Jaccard similarity threshold of 0.7, which proved to be efficient for both SantaCoder and StarCoder models. A high threshold reduces false positives, leading to fewer unnecessary comparisons and lower computational overhead. Moreover, this standard threshold value has been shown to be robust for duplicate detection.

Instead of removing exact and near duplicates found against other open datasets, we add a boolean mask to our dataset. This approach enhances reproducibility by allowing researchers to filter the dataset for unique files, according to their specific requirements.

The final dataset structure is shown in the example below.

{
    "file_name": "Font.java",
    "file_path": ".../lateralgm/resources/Font.java",
    "content": "*/ package org.lateralgm.resources; import java.util.EnumMap; import org.lateralgm.main.Prefs; ...",
    "file_size": 1,985,
    "language": "Java",
    "extension": ".java",
    "repo_name": "lwizchz/GameMaker-HTML5-Player",
    "repo_stars": 22,
    "repo_forks": 9,
    "repo_open_issues": 0,
    "repo_created_at": "2011-09-10T16:05:20Z",
    "repo_pushed_at": "2013-05-06T23:00:17Z",
    "sha": "00046809b218b2c058f4be7...",
    "exact_duplicates_stackv1": False,
    "exact_duplicates_stackv2": True,
    "near_duplicates_stackv1": True,
    "near_duplicates_stackv2": False,
     ....
 
  }

Dataset Fields

file_name: name of the file extracted from its repo
file_path: path to the file in its repo
content: content of the file
file_size: size of the file
language: language of the file
extension: language extension of the file
repo_name: complete name of the file's repo
repo_stars: number of stars of the file's repo
repo_forks: number of forks of the file's repo
repo_open_issues: number of open issues of the file's repo at the extraction date
repo_created_at: creation date of the file's repo
repo_pushed_at: date of the most recent push to the file's repo until the extraction date
sha: sha value of the file's content
exact_duplicates_pubdataset: boolean flag stating if there are any exact duplicate files found against another public dataset (The Stackv2, The Stack, RedPajama, GithubCode, CodeParrot)
near_duplicates_pubdataset: boolean flag stating if there are any near duplicate files found against another public dataset (The Stackv2, The Stack, RedPajama, GithubCode, CodeParrot)

The distribution of the languages in The Heap is presented in the table below. The third column shows the number of files collected after filtering based on file size and word count. The last column indicates the number of files remaining after removing exact duplicates within the dataset, with exact and near duplicates compared to other datasets flagged among the remaining files.

Programming languages included in The Heap

Language	Repositories	Raw Files	Unique Files
Ada	676	41,367	35,425
Agda	142	5,483	5,113
ANTLR	101	564	541
Apex	254	17,833	13,641
Assembly	7,100	208,896	104,911
Awk	1,191	16,586	15,620
C#	50,000	5,906,716	3,770,829
C++	50,000	14,891,856	8,341,620
Clojure	27,107	380,567	273,181
COBOL	241	2,242	1,208
Common Lisp	796	45,083	16,968
Cuda	11,907	54,137	26,175
Crystal	368	11,606	7,300
D	1,191	26,048	13,359
Dart	1,185	185,630	128,111
Elixir	11,907	484,935	413,203
Elm	1,477	15,511	12,384
Erlang	2,475	453,856	127,910
F#	277	8,260	7,963
Forth	1,240	55,932	32,049
Fortran	876	22,152	16,015
Groovy	222	28,287	7,932
Hack	2,198	60,299	48,353
Haskell	1,279	84,916	37,405
JavaScript	8,023	122,788	111,234
Julia	50,000	6,989,601	3,757,338
Kotlin	2,959	46,284	38,381
Less	21,665	1,467,343	1,389
Lisp	433	17,276	7,389
Lua	42,241	4,605,230	912,898
Mathematica	1,528	164,498	89,853
MATLAB	20,828	1,051,354	665,659
NetLogo	332	900	863
NewLisp	35	5,819	5,148
Nix	1,892	75,093	71,199
Objective-C	7,700	1,899,714	698,137
OCaml	1,761	321,989	95,171
Pascal	5,218	130,832	225,749
Perl	50,000	1,798,520	269,760
PHP	50,000	12,707,727	3,363,040
Processing	2,950	24,723	20,343
Prolog	1,071	38,995	20,279
Python	50,000	2,290,182	1,792,451
R	44,993	389,139	374,812
Racket	158	1,384	1,306
Ruby	13,378	1,579,635	794,364
Rust	42,847	2,496,177	844,258
Scala	5,893	749,370	224,021
Scheme	1,878	106,620	53,226
Shell	150	47,531	1,084
SQL	130	47,185	41,178
Swift	13,924	633,819	439,565
Vue	14,858	834	498
WebAssembly	68	834	587
Total	733,663	96,990,250	38,681,690

Usage

Using the Datasets API, our dataset can be used as follows:

from datasets import load_dataset

dataset_name = 'redpajama'
language = 'Python'

ds = load_dataset(
    "WizzF/Heap-Forge",
    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}'])