datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
arxiv_cplusplus_research_code
Dataset card for ArtifactAI/arxiv_cplusplus_research_code
Dataset Description
https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_cplusplus_research_code
Dataset Summary
ArtifactAI/arxiv_python_research_code contains over 10.6GB of source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs.
How to use it
from datasets import load_dataset
# full dataset (10.6GB of data)
ds =… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_cplusplus_research_code.arxiv_python_research_code
Dataset Card for "ArtifactAI/arxiv_python_research_code"
Dataset Description
https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_python_research_code
Dataset Summary
AlgorithmicResearchGroup/arxiv_python_research_code contains over 4.13GB of source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs.
How to use it
from datasets import load_dataset
# full dataset (4.13GB of data)
ds =… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_python_research_code.arxiv_research_code
Dataset Card for "AlgorithmicResearchGroup/arxiv_research_code"
Dataset Description
https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_research_code
Dataset Summary
ArtifactAI/arxiv_research_code contains over 21.8GB of source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs.
How to use it
from datasets import load_dataset
# full dataset (21.8GB of data)
ds =… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_research_code.arxiv_deep_learning_python_research_code
ArXiv Deep Learning Python Research Code
A curated corpus of Python source code files extracted from GitHub repositories referenced in ArXiv papers. Contains 391,496 files (1.49 GB) filtered to deep learning frameworks, designed for training and evaluating Code LLMs on research-grade code.
Dataset Summary
Statistic
Value
Total files
391,496
Total size
1.49 GB
Source repos
34,099
Time span
ArXiv inception through July 2023
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_deep_learning_python_research_code.advanced-quantum-algorithms
Neura Parse — Advanced Quantum Algorithms: Derivations, QSVT/Block-Encoding & Hamiltonian Simulation
A derivation- and resource-analyzed algorithms vertical spanning the canonical fault-tolerant canon (with full proofs, complexity, and worked traces) and the modern QSVT/block-encoding toolkit through Hamiltonian simulation, amplitude estimation, and quantum linear systems. Turns the general dataset's one-topic-per-algorithm summaries into line-by-line derivations, lower… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/advanced-quantum-algorithms.aria-repo-benchmark
ARIA Repo Benchmark
The ARIA Repo Benchmark is part of the ARIA benchmark suite, a collection of closed-book benchmarks probing the ML knowledge that frontier models have internalized during training. This dataset contains 58 curated research paper implementations with metadata for evaluating whether ML experiments described in papers can be reproduced.
Dataset Summary
Size: 58 entries
Coverage: Computer Vision, NLP, Time Series, Graph, Bioinformatics
Purpose:… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/aria-repo-benchmark.