datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
github-codereview
Code Review Dataset
A large-scale dataset of the best human-written code reviews from top GitHub repositories.
Each row captures a moment where a human code reviewer left an inline comment on a pull request, and the author subsequently modified the code in response.
The dataset also includes negative examples — code from the same PRs that passed review without comments — to help models learn when code is acceptable.
This provides a natural signal for training models to:
Generate… See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/github-codereview.codereviewercode-review-bench
Code Review Bench
A paired online-offline benchmark for AI code review.
Splits
online — Stratified sample of 1,135 bot-reviewed PRs, scraped from open-source Github repositories and scored by the online benchmark (15 tools, Feb–Apr 2026).
offline — 136 expert-curated golden issues across 50 PRs (5 repositories).
Provenance
The offline golden issues extend the 50-PR benchmark originally created by Greptile (2025) and refined by Augment (2025). Our… See the full description on the dataset page: https://huggingface.co/datasets/code-review-bench/code-review-bench.amazon-reviews-for-llm
Cross-domain sequential recommendation dataset
A sequential recommendation dataset drawn from Amazon Reviews 2023, covering
Books, CDs_and_Vinyl, Movies_and_TV, Video_Games.
Each row of interactions.parquet is one user buying or reviewing one item at one time.
Users are sampled so that every one of them is active in all domains, their
interactions are ordered chronologically and cut into train/valid/test, and each
interaction carries a fixed set of 10 candidate items for ranking… See the full description on the dataset page: https://huggingface.co/datasets/sungjin-code/amazon-reviews-for-llm.amazon-reviews-books-for-llm
Books sequential recommendation dataset
A sequential recommendation dataset drawn from Amazon Reviews 2023, covering
Books.
Each row of interactions.parquet is one user buying or reviewing one item at one time.
Users are sampled so that every one of them is active in all domains, their
interactions are ordered chronologically and cut into train/valid/test, and each
interaction carries a fixed set of 10 candidate items for ranking
evaluation. Integer user_idx / item_idx columns… See the full description on the dataset page: https://huggingface.co/datasets/sungjin-code/amazon-reviews-books-for-llm.amazon-reviews-for-llm-extended
Cross-domain sequential recommendation dataset
A sequential recommendation dataset drawn from Amazon Reviews 2023, covering
Books, CDs_and_Vinyl, Movies_and_TV, Video_Games.
Each row of interactions.parquet is one user buying or reviewing one item at one time.
Users are sampled so that every one of them is active in all domains, their
interactions are ordered chronologically and cut into train/valid/test, and each
interaction carries a fixed set of 10 candidate items for ranking… See the full description on the dataset page: https://huggingface.co/datasets/sungjin-code/amazon-reviews-for-llm-extended.github-codereview-dataset
Github-Codereview-Dataset
Made with ❤️ using 🦥 Unsloth Studio
github-codereview-dataset was generated with Unsloth Recipe Studio. It contains 10,000 generated records.
🚀 Quick Start
from datasets import load_dataset
# Load the main dataset
dataset = load_dataset("manishsaini1/github-codereview-dataset", "data", split="train")
df = dataset.to_pandas()
📊 Dataset Summary
📈 Records: 10,000
📋 Columns: 23
📋 Schema & Statistics… See the full description on the dataset page: https://huggingface.co/datasets/manishsaini1/github-codereview-dataset.b2_code_fasttext_pos_codeforces_neg_codereviewagent-traces-code-review-pipeline
Agent Traces: code-review-pipeline
Synthetic multi-agent workflow traces with LLM-enriched content for the code-review-pipeline domain.
Part of the juliensimon/open-agent-traces collection — 10 datasets covering diverse domains and workflow patterns.
What is this dataset?
This dataset contains 2,035 events across 50 workflow runs, each representing a complete multi-agent execution trace. Every trace includes:
Agent reasoning — chain-of-thought for each agent step
LLM… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/agent-traces-code-review-pipeline.arabic-english-code-switching-review-annotations
Review Annotations for Arabic-English Code-Switching Speech
This metadata-only dataset publishes review decisions and transcript-correction deltas for MohamedRashad/arabic-english-code-switching. It contains no human audio, no local file paths, no raw review notes, and no copies of unchanged upstream transcripts.
The annotations are pinned to upstream revision 4a3bffc45219c35949470de32b8d4cb328b0ce11 and join by upstream_row_index.
Coverage and outcomes
The… See the full description on the dataset page: https://huggingface.co/datasets/abdo1819/arabic-english-code-switching-review-annotations.dahoas-code_review_instruct_critique_revision_python_standardizedb2_code_fasttext_pos_codeforces_neg_codereview_1k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_codereview_1k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
15.3
50.8
72.6
27.0
40.0
38.7
29.7
6.3
9.1
AIME24
Average Accuracy: 15.33% ± 1.35%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
20.00%
6
30
2
13.33%
4
30… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_codereview_1k_eval_636d.b2_code_fasttext_pos_codeforces_neg_codereview_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_codereview_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
23.3
60.2
78.0
30.2
42.2
41.4
45.3
14.7
19.0
AIME24
Average Accuracy: 23.33% ± 1.05%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
23.33%
7
30
2
20.00%
6
30… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_codereview_eval_636d.a1_code_stackexchange_codereview_1744643682_eval_1331
mlfoundations-dev/a1_code_stackexchange_codereview_1744643682_eval_1331
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
GPQADiamond
JEEBench
MMLUPro
LiveCodeBench
CodeElo
Accuracy
16.3
55.8
76.2
46.3
39.1
29.0
25.2
6.5
AIME24
Average Accuracy: 16.33% ± 1.52%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
26.67%
8
30
2
16.67%
5
30
3
10.00%
3
30
4… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/a1_code_stackexchange_codereview_1744643682_eval_1331.a1_code_stackexchange_codereview_1744693103_eval_1331
mlfoundations-dev/a1_code_stackexchange_codereview_1744693103_eval_1331
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
GPQADiamond
JEEBench
MMLUPro
LiveCodeBench
CodeElo
Accuracy
18.0
52.5
74.8
42.8
40.3
28.6
25.6
6.8
AIME24
Average Accuracy: 18.00% ± 1.35%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
26.67%
8
30
2
20.00%
6
30
3
13.33%
4
30
4… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/a1_code_stackexchange_codereview_1744693103_eval_1331.a1_code_stackexchange_codereview_eval_636d
mlfoundations-dev/a1_code_stackexchange_codereview_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
18.0
53.8
73.4
28.0
38.8
42.6
25.8
6.1
8.6
AIME24
Average Accuracy: 18.00% ± 1.71%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
26.67%
8
30
2
23.33%
7
30
3
16.67%
5
30… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/a1_code_stackexchange_codereview_eval_636d.b2_code_fasttext_pos_codeforces_neg_codereview_10k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_codereview_10k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
20.7
60.2
75.8
27.2
42.9
36.9
40.0
11.4
15.8
AIME24
Average Accuracy: 20.67% ± 1.55%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
20.00%
6
30
2
26.67%
8… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_codereview_10k_eval_636d.b2_code_fasttext_pos_codeforces_neg_codereview_0.3k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_codereview_0.3k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
17.0
57.0
70.4
28.2
42.3
37.7
25.4
6.6
9.3
AIME24
Average Accuracy: 17.00% ± 1.37%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
16.67%
5
30
2
6.67%
2
30… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_codereview_0.3k_eval_636d.b2_code_fasttext_pos_codeforces_neg_codereview_3k_eval_636d
mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_codereview_3k_eval_636d
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
Accuracy
19.7
56.2
75.6
27.8
43.5
41.8
35.7
11.7
12.4
AIME24
Average Accuracy: 19.67% ± 1.37%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
16.67%
5
30
2
13.33%
4
30… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/b2_code_fasttext_pos_codeforces_neg_codereview_3k_eval_636d.b2_code_fasttext_pos_codeforces_neg_codereview_10kb2_train_fasttext_pos_codeforces_neg_codereviewb2_code_fasttext_pos_codeforces_neg_codereview_0.3kb2_code_fasttext_pos_codeforces_neg_codereview_1kb2_code_fasttext_pos_codeforces_neg_codereview_3kmovie_review