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Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

modalityAllTextImageAudioTabularVideo
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sizeAllUnder 1K1K – 10K100K – 1M1M – 10M
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01ronantakizawa /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.tabulartext-generation100K<n<1M62 likes1.4k downloads7mo agoHugging Face02code-review-bench /code-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.tabulartext-generation1K<n<10K1 likes115 downloads2mo agoHugging Face03manishsaini1 /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.tabular10K<n<100K1 likes82 downloads9d agoHugging Face04sungjin-code /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.tabular1K<n<10K0 likes68 downloads29d agoHugging Face05mlfoundations-dev /b2_code_fasttext_pos_codeforces_neg_codereviewtabular10K<n<100K0 likes38 downloads1y agoHugging Face06sungjin-code /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.tabular1K<n<10K0 likes33 downloads1mo agoHugging Face07sungjin-code /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.tabular10K<n<100K0 likes29 downloads1mo agoHugging Face08juliensimon /agent-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.tabular1K<n<10K1 likes21 downloads6mo agoHugging Face09mlfoundations-dev /b2_code_fasttext_pos_codeforces_neg_codereview_10ktabular10K<n<100K0 likes11 downloads1y agoHugging Face10mlfoundations-dev /b2_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.tabular1K<n<10K0 likes11 downloads1y agoHugging Face11mlfoundations-dev /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.tabular1K<n<10K0 likes9 downloads1y agoHugging Face12mlfoundations-dev /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.tabular1K<n<10K0 likes7 downloads1y agoHugging Face13mlfoundations-dev /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.tabular1K<n<10K0 likes7 downloads1y agoHugging Face14mlfoundations-dev /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.tabular1K<n<10K0 likes7 downloads1y agoHugging Face15mlfoundations-dev /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.tabular1K<n<10K0 likes7 downloads1y agoHugging Face16mlfoundations-dev /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.tabular1K<n<10K0 likes7 downloads1y agoHugging Face17YoungPhlo /dahoas-code_review_instruct_critique_revision_python_standardizedtabular10K<n<100K1 likes6 downloads3y agoHugging Face18mlfoundations-dev /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.tabular1K<n<10K0 likes6 downloads1y agoHugging Face19mlfoundations-dev /b2_train_fasttext_pos_codeforces_neg_codereviewtabularn<1K0 likes4 downloads1y agoHugging Face20mlfoundations-dev /b2_code_fasttext_pos_codeforces_neg_codereview_0.3ktabularn<1K0 likes4 downloads1y agoHugging Face21mlfoundations-dev /b2_code_fasttext_pos_codeforces_neg_codereview_1ktabular1K<n<10K0 likes4 downloads1y agoHugging Face22mlfoundations-dev /b2_code_fasttext_pos_codeforces_neg_codereview_3ktabular1K<n<10K0 likes4 downloads1y agoHugging Face

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