datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
CodeGen-Diverse-5K
CodeGen-Diverse-5K: Broad Coverage for Competitive Programming
Part of the CodeGen suite | CodeGen-Deep-5K (sister dataset)
Dataset Description
CodeGen-Diverse-5K is a broad coverage dataset designed for training code generation models across a wide variety of competitive programming problems. This dataset prioritizes problem diversity over solution diversity, covering 5,000 unique problems with consistent, high-quality solutions.
Key Statistics
Total samples:… See the full description on the dataset page: https://huggingface.co/datasets/Naholav/CodeGen-Diverse-5K.CodeGen-Deep-5K
CodeGen-Deep-5K: Deep Reasoning for Competitive Programming
Part of the CodeGen suite | CodeGen-Diverse-5K (sister dataset)
Dataset Description
CodeGen-Deep-5K is a deep reasoning dataset designed for training code generation models with enhanced problem-solving capabilities. Unlike traditional datasets, this generates multiple distinct solutions for each problem, providing varied reasoning traces and approaches.
Key Statistics
Total samples: 5,000
Unique… See the full description on the dataset page: https://huggingface.co/datasets/Naholav/CodeGen-Deep-5K.code_generation_lite-th
LiveCodeBench code_generation_lite, Thai
111 competitive-programming problems from LeetCode and AtCoder, with the problem
statement translated to Thai. Everything else — test cases, starter code, metadata —
is the upstream value unchanged.
Known defects
The line breaks are gone from the problem statements. 110 of the 111 rows have no
line break at all in question_content; the one remaining row has two. These are
competitive-programming statements whose input and… See the full description on the dataset page: https://huggingface.co/datasets/iapp/code_generation_lite-th.LiveCodeBench-CodeGenerationry_manim_codegen
RY Manim Code Generation
This dataset contains queries and responses for ideal Manim code. The input and output have been preprocessed to keep context clean.
Format:
Jsonl format used instead of Json for better efficiency.
{"query": "<a user's question about a topic>", "output": "<LLM's manim code>"}
Citations:
This repository includes data sourced and processed from the following datasets:… See the full description on the dataset page: https://huggingface.co/datasets/RY7Games/ry_manim_codegen.codegen-th
Summary
This is a 🇹🇭 Thai-translated (GCP) dataset based on 4.5K codegen instruction dataset GPTeacher
Supported Tasks:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: Thai
Version: 1.0
code_generation