datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
doocs-leetcode-solutions
Doocs LeetCode Solutions
LeetCode problems with solutions in many programming languages, built from the Doocs LeetCode repository. Each solution comes with the approach name, the reasoning that leads to it, and an explanation with complexity analysis. The dataset is meant for fine-tuning and evaluating code generation models.
The dataset is regenerated monthly from the latest Doocs commit by the leetcode-dataset-generator tool.
Structure
The dataset has two… See the full description on the dataset page: https://huggingface.co/datasets/olegshulyakov/doocs-leetcode-solutions.leetcode-problem-detailed
LeetCode Scraper Dataset
This dataset contains information scraped from LeetCode, including problem details, metadata, and related files. It is designed to assist developers in analyzing LeetCode problems, generating insights, and building tools for competitive programming or educational purposes.
questions_deets.csv
Contains detailed information about each problem, including problem descriptions, constraints, and examples.
Columns:
questionFrontendId: Unique problem ID.… See the full description on the dataset page: https://huggingface.co/datasets/kaysss/leetcode-problem-detailed.leetcode-complete
Complete LeetCode Problems Dataset
This dataset contains a comprehensive collection of LeetCode problems (including premium) with AI-generated solutions in JSONL format. It is regularly updated to include new problems as they are added to LeetCode.
Splits
The dataset is divided into the following splits:
train: Contains approximately 80% of the problems for training
validation: Contains approximately 10% of the problems for validation
test: Contains approximately… See the full description on the dataset page: https://huggingface.co/datasets/whiskwhite/leetcode-complete.leetcode-problem-solutions
LeetCode Solution Dataset
This dataset contains community-contributed LeetCode solutions scraped from public discussions and solution pages, enriched with metadata such as vote counts, author info, tags, and full code content. The goal is to make high-quality, peer-reviewed coding solutions programmatically accessible for research, analysis, educational use, or developer tooling.
Column Descriptions
Column Name
Type
Description
question_slug
string
The unique… See the full description on the dataset page: https://huggingface.co/datasets/kaysss/leetcode-problem-solutions.leetcode-python-solutions-with-exaplanationsleetcode-assembly
LeetCode Assembly Dataset
441 LeetCode problems solved in C, compiled to assembly across 4 architectures, 2 compilers, and 4 optimization levels using GCC and Clang via the Godbolt Compiler Explorer API.
Dataset Summary
Stat
Value
Total rows
14,112
Unique problems
441
Architectures
x86-64, AArch64, MIPS64, RISC-V 64
Compilers
GCC 15.2, Clang 21.1.0
Optimization levels
-O0, -O1, -O2, -O3
Compilation success rate
100%
Difficulty split
Easy: 98… See the full description on the dataset page: https://huggingface.co/datasets/ronantakizawa/leetcode-assembly.leetcode_with_youtube_captionsvgrout-leetcode-teacher-demos
vGROUT LeetCode teacher demonstrations
Cached teacher demonstrations used to warm up the
vGROUT gradient-routing experiments on the
ariahw/rl-rewardhacking LeetCode
environment. Each row is a full problem-specific completion. The kind column gives the
two demonstration types:
hack (215 rows): verified exploits of the run_tests loophole (hacked=True,
gt_pass=False).
solve (126 rows): correct solutions verified against the ground-truth tests
(gt_pass=True).
Why fewer… See the full description on the dataset page: https://huggingface.co/datasets/wassname/vgrout-leetcode-teacher-demos.LeetCode_YT_CC_CoT_SummaryLeetCode Information & YouTube Captions with CoT Summaries
Original data -> LimYeri/leetcode_with_youtube_captions
The original ['cc_content'] column had tokens that were too long and contained a lot of repetition, which necessitated summarization.
Consequently, our team (Project Team: CodeMind) summarized the ['cc_content'] column data using the Chain of Thought (CoT) technique with the gpt-3.5-turbo-0125 & gpt-4-turbo-2024-04-09 model. -> new column ['Summary']