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---
dataset_info:
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configs:
- config_name: Assembly
data_files:
- split: train
path: Assembly/train-*
- config_name: C
data_files:
- split: train
path: C/train-*
- config_name: C#
data_files:
- split: train
path: C#/train-*
- config_name: C++
data_files:
- split: train
path: C++/train-*
- config_name: Dart
data_files:
- split: train
path: Dart/train-*
- config_name: Erlang
data_files:
- split: train
path: Erlang/train-*
- config_name: Go
data_files:
- split: train
path: Go/train-*
- config_name: Groovy
data_files:
- split: train
path: Groovy/train-*
- config_name: Haskell
data_files:
- split: train
path: Haskell/train-*
- config_name: Java
data_files:
- split: train
path: Java/train-*
- config_name: JavaScript
data_files:
- split: train
path: JavaScript/train-*
- config_name: Julia
data_files:
- split: train
path: Julia/train-*
- config_name: Kotlin
data_files:
- split: train
path: Kotlin/train-*
- config_name: PHP
data_files:
- split: train
path: PHP/train-*
- config_name: Perl
data_files:
- split: train
path: Perl/train-*
- config_name: PowerShell
data_files:
- split: train
path: PowerShell/train-*
- config_name: Python
data_files:
- split: train
path: Python/train-*
- config_name: R
data_files:
- split: train
path: R/train-*
- config_name: Ruby
data_files:
- split: train
path: Ruby/train-*
- config_name: Rust
data_files:
- split: train
path: Rust/train-*
- config_name: Scala
data_files:
- split: train
path: Scala/train-*
- config_name: Shell
data_files:
- split: train
path: Shell/train-*
- config_name: Swift
data_files:
- split: train
path: Swift/train-*
- config_name: TypeScript
data_files:
- split: train
path: TypeScript/train-*
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- code
- multilingual
- github
- commits
size_categories:
- 1M<n<10M
---
<div align="center">
# Themis-Git-Commits-Merged
[![arXiv](https://img.shields.io/badge/arXiv-2605.00754-b31b1b.svg)](https://arxiv.org/abs/2605.00754)
[![Models](https://img.shields.io/badge/%F0%9F%A4%97%20Models-Themis--RM-yellow)](https://huggingface.co/collections/project-themis/themis-reward-model-collection)
[![Datasets & Benchmarks](https://img.shields.io/badge/%F0%9F%A4%97%20Datasets%20%26%20Benchmarks-Themis-blue)](https://huggingface.co/collections/project-themis/themis-preference-datasets-and-benchmarks)
[![GitHub](https://img.shields.io/badge/GitHub-Themis-181717?logo=github)](https://github.com/iNeil77/Themis)
[![Docker](https://img.shields.io/badge/Docker-ineil77%2Fthemis-2496ED?logo=docker)](https://hub.docker.com/repository/docker/ineil77/themis/general)
</div>
## Overview
**Themis-Git-Commits-Merged** is a large-scale dataset of ~3.98M single-file code commits from **permissively licensed** GitHub repositories that have been cross-referenced with [GHTorrent](https://ghtorrent.org/) pull request data to retain only commits that are part of **successfully merged, non-reverted pull requests**. This provides implicit human validation of each code change — a merge decision by project maintainers confirms the intent and quality of the commit.
This dataset is derived from [Themis-Git-Commits](https://huggingface.co/datasets/project-themis/git-commits) (the raw, unfiltered commit pool) by joining against GHTorrent pull request records through **end of 2021** (the temporal extent of the GHTorrent PR data used). It serves as the direct input to the aspect classification and preference construction stages of the [Themis](https://github.com/iNeil77/Themis) pipeline, which produces the commit-based preference pairs in [Themis-CodePreference](https://huggingface.co/datasets/project-themis/Themis-CodePreference) and [Themis-CodeRewardBench](https://huggingface.co/datasets/project-themis/Themis-CodeRewardBench).
The SQL query used for initial commit mining restricts to repositories under permissive open-source licenses only (MIT, Apache-2.0, BSD-2/3-Clause, ISC, CC0-1.0, EPL-1.0, MPL-2.0, Unlicense, AGPL-3.0, LGPL-2.1, Artistic-2.0). The BigQuery snapshot used contains commits up to **early 2022** — predating the widespread availability of LLM code generation tools — ensuring that all code changes in the dataset represent **genuine human-authored preferences**.
## Relationship to Themis-Git-Commits
<div align="center">
| | [Themis-Git-Commits](https://huggingface.co/datasets/project-themis/git-commits) | **Themis-Git-Commits-Merged** (this dataset) |
|:---|:---|:---|
| **Scope** | All mined single-file commits | Only commits in merged, non-reverted PRs |
| **Extension filtering** | No | Yes (file extension matches target language) |
| **PR validation** | No | Yes (via GHTorrent, through end of 2021) |
| **Human validation** | Implicit (authored by humans) | Explicit (merge decision by maintainers) |
| **Downstream use** | Raw pool for further filtering | Direct input to aspect classification |
</div>
## Collection Pipeline
The commit mining pipeline is described in detail in the [Themis paper](https://arxiv.org/abs/2605.00754) and the [Dataset](https://github.com/iNeil77/Themis/tree/main/Dataset) folder in the GitHub repository. The BigQuery SQL query and scraping infrastructure are modified from the [OctoPack](https://arxiv.org/abs/2308.07124) pipeline ([CommitPack](https://huggingface.co/datasets/bigcode/commitpack)); the subsequent filtering, classification, and preference construction stages are original to Themis.
1. **BigQuery Mining** — A [GoogleSQL query](https://github.com/iNeil77/Themis/blob/main/Dataset/Commit_Mining_SQL/consolidated_query.sql) (modified from [OctoPack](https://arxiv.org/abs/2308.07124)) extracts single-file commits from **permissively licensed** repositories in `bigquery-public-data.github_repos`, filtering for target programming languages and non-trivial commit messages.
2. **Repository Reputation Filtering** — Commits are subset to those originating from [curated high-reputation repositories](https://github.com/iNeil77/Themis/tree/main/Dataset/Repos) (15+ GitHub stars, 5+ contributors, 10+ issues).
3. **Extension Filtering** — Commits are further filtered so the changed file's extension matches a target programming language.
4. **Content Retrieval** — The pre-commit (`old_contents`) and post-commit (`new_contents`) file contents are fetched from GitHub via shallow git fetches using [retrieve_commit_contents.py](https://github.com/iNeil77/Themis/blob/main/Dataset/Utils/retrieve_commit_contents.py).
5. **MinHash Deduplication** — Near-duplicate content is removed using [MinHash LSH deduplication](https://github.com/iNeil77/Themis/blob/main/Dataset/Utils/minHash_dedupe_local.py) (shingle size 5, 256 permutations, Jaccard threshold 0.7).
6. **Pull Request Cross-Referencing** *(this step produces this dataset)* — Commits are joined with [GHTorrent](https://ghtorrent.org/) pull request data (through **end of 2021**) to retain only non-reverted commits that are part of successfully merged pull requests from reputable repositories, ensuring implicit human validation of each code change.
## Downstream Processing (Not in This Dataset)
The steps below are applied downstream to produce the final preference pairs in Themis-CodePreference and Themis-CodeRewardBench, and are **not** reflected in this dataset:
- **Language Subsetting** — The 24 languages in this dataset are narrowed to the 8 target languages (C, C#, C++, Go, Java, JavaScript, Python, Ruby) used in [Themis-CodePreference](https://huggingface.co/datasets/project-themis/Themis-CodePreference) and [Themis-CodeRewardBench](https://huggingface.co/datasets/project-themis/Themis-CodeRewardBench).
- **Temporal Subsetting** — For training data ([Themis-CodePreference](https://huggingface.co/datasets/project-themis/Themis-CodePreference)), only commits pushed before **March 2019** are retained. For benchmark data ([Themis-CodeRewardBench](https://huggingface.co/datasets/project-themis/Themis-CodeRewardBench)), commits are scoped to **June 2019 – January 2021** from disjoint repositories.
- **Aspect Classification** — Commits are assigned to quality dimensions (Functional Correctness, Runtime Efficiency, Memory Efficiency, Security Hardness, Readability & Maintainability) using criteria-specialized [ModernBERT](https://huggingface.co/answerdotai/ModernBERT-base) commit classifiers, trained on seed positives retrieved via [curated term lists](https://github.com/iNeil77/Themis/tree/main/Dataset/Commit_Mining_Terms).
- **LLM Scoring & Instruction Synthesis** — Frontier LMs validate preference strength and generate realistic inverse instructions.
- **LLM-as-a-Judge Preference Labelling** — Multi-sample voting with frontier LMs produces consensus preference labels.
## Dataset Schema
<div align="center">
| Column | Type | Description |
|:---|:---:|:---|
| `commit` | string | Git commit SHA |
| `subject` | string | First line of the commit message |
| `message` | string | Full commit message body |
| `repos` | string | Comma-separated list of repository names containing this commit |
| `file_path` | string | Path of the changed file |
| `license` | string | SPDX license identifier of the source repository |
| `unix_time` | int64 | Committer timestamp (seconds since epoch) |
| `new_contents` | string | File contents after the commit (post-commit) |
| `old_contents` | string | File contents before the commit (pre-commit) |
</div>
## Language Distribution
The dataset is partitioned by programming language, with one config per language. Each config has a single `train` split.
<div align="center">
| Language | Commits | | Language | Commits |
|:---|---:|:---|:---|---:|
| Python | 914,849 | | Rust | 57,383 |
| JavaScript | 694,051 | | Scala | 46,557 |
| Java | 402,369 | | Swift | 40,271 |
| Ruby | 345,124 | | Groovy | 16,723 |
| PHP | 281,390 | | PowerShell | 16,729 |
| C++ | 194,218 | | Kotlin | 16,467 |
| Go | 189,131 | | Erlang | 15,623 |
| C | 172,736 | | Haskell | 13,851 |
| C# | 138,000 | | Dart | 8,890 |
| TypeScript | 114,769 | | Perl | 4,930 |
| Shell | 95,305 | | Julia | 2,662 |
| | | | R | 656 |
| | | | Assembly | 507 |
| **Total** | | | | **~3.98M** |
</div>
## Filters Applied
All filters from the upstream [Themis-Git-Commits](https://huggingface.co/datasets/project-themis/git-commits) pipeline apply, plus the PR merge filter:
<div align="center">
| Filter | Purpose |
|:---|:---|
| **License allowlist** | MIT, Apache-2.0, BSD-2-Clause, BSD-3-Clause, ISC, CC0-1.0, EPL-1.0, MPL-2.0, Unlicense, AGPL-3.0, LGPL-2.1, Artistic-2.0 |
| **Language allowlist** (SQL) | Python, Java, JavaScript, C, C#, C++, TypeScript, Go, Ruby (repo-level filter in BigQuery query) |
| **Message length** | 10 < length < 15,000 characters |
| **Message blocklist** | ~50 low-signal messages excluded (e.g., "initial commit", "wip", "yolo") |
| **Pattern exclusion** | Merge commits and CI push messages filtered out |
| **Same-path constraint** | `old_path = new_path` — file was modified in place, not renamed or moved |
| **Single-file constraint** | Commit touches exactly one file |
| **Content retrieval** | Both pre-commit and post-commit file contents successfully fetched |
| **Near-deduplication** | MinHash LSH with Jaccard threshold 0.7 |
| **Extension filtering** | Changed file's extension mapped to one of 24 target programming languages (see distribution table) |
| **PR merge filter** | Commit is part of a successfully merged, non-reverted pull request (GHTorrent data through end of 2021) |
</div>
## Usage
```python
from datasets import load_dataset
# Load a single language
python_commits = load_dataset("project-themis/git-commits-merged", "Python")
sample = python_commits["train"][0]
print(f"Commit: {sample['commit']}")
print(f"Subject: {sample['subject']}")
print(f"License: {sample['license']}")
print(f"File: {sample['file_path']}")
# Load all languages
for lang in ["C", "CSharp", "Cpp", "Go", "Java", "JavaScript", "Python", "Ruby", "TypeScript"]:
ds = load_dataset("project-themis/git-commits-merged", lang)
print(f"{lang}: {len(ds['train'])} commits")
```
## License
This dataset is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). The source commits are drawn exclusively from repositories with permissive open-source licenses (see filter table above).
## Citation
```bibtex
@article{themis2025,
title={Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring},
author={Paul, Indraneil and Gurevych, Iryna and Glava\v{s}, Goran},
journal={arXiv preprint arXiv:2605.00754},
year={2025}
}
```