datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
s2orc_full
S2ORC Full — Semantic Scholar Open Research Corpus
A complete redistribution of the S2ORC dataset in Parquet format on Hugging Face, containing 14.5 million academic papers with full text, structured metadata, and citation information.
Dataset Description
S2ORC (Semantic Scholar Open Research Corpus) is a general-purpose corpus for NLP and text mining research over scientific papers, originally developed by the Allen Institute for AI. This version provides the full… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/s2orc_full.arxiv_s2orc_parsed
Dataset Card for "ArtifactAI/arxiv_s2orc_parsed"
Dataset Description
https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_s2orc_parsed
Dataset Summary
AlgorithmicResearchGroup/arxiv_s2orc_parsed is a subset of the AllenAI S2ORC dataset, a general-purpose corpus for NLP and text mining research over scientific papers,
The dataset is filtered strictly for ArXiv papers, including the full text for each paper. Github links have been extracted… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_s2orc_parsed.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.s2orc_arxiv
S2ORC ArXiv
A subset of the Semantic Scholar Open Research Corpus (S2ORC) filtered to ArXiv papers. Contains 2.58 million parsed scientific papers with full text, abstracts, structured sections, figures, and citation metadata.
Dataset Summary
Statistic
Value
Total papers
2,579,762
Total size
~266 GB
Format
Parquet
Split
train
Dataset Structure
Content Fields
Field
Type
Description
title
string
Paper title
abstract… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/s2orc_arxiv.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.multi-strategy-algorithmic-tasks
Multi-Strategy Algorithmic Tasks
A synthetic benchmark of parseable algorithmic problems with multiple valid
solution strategies for each task. Each example contains a problem,a strategy-specific
solution trace, and the strategy used to generate that trace.
The benchmark accompanies
Uncovering Latent Reasoning Strategies in Language Models,
which studies the problem of recovering mixtures of strategies implicitly represented in language models.
The benchmark provides a… See the full description on the dataset page: https://huggingface.co/datasets/awni00/multi-strategy-algorithmic-tasks.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.arxiv_nlp_intstruct
Dataset Card for "arxiv_nlp_intstruct"
Dataset Summary
The "AlgorithmicResearchGroup/arxiv_nlp_intstruct" dataset consists of question-answer pairs derived from ArXiv abstracts from the cs.CL category".
Questions and answers are generated using GPT-3.5-turbo model
Languages
English
Dataset Structure
Data Instances
train
Size of downloaded dataset files: 38.4 MB
An example of 'train' looks as follows.
{
"question": "What… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_nlp_intstruct.algorithmic-reasoning-seed
Dataset Card for Algorithmic Reasoning (seed)
Note: This dataset is WIP and most question's answer section is empty or incomplete! See also "Other Known Limitations" section
Warning: If you somehow do use this dataset, remember to NOT do any eval after training on the questions in this dataset!
Dataset Summary
Dataset to help LLM learn how to reason about code, especially on algorithmic tasks, by seeing human demostration.
Supported Tasks and Leaderboards
[More… See the full description on the dataset page: https://huggingface.co/datasets/lemonteaa/algorithmic-reasoning-seed.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.