datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
severity_ablation_logicLogicVistaLogicNLI
Dataset Card for "LogicNLI"
@inproceedings{tian-etal-2021-diagnosing,
title = "Diagnosing the First-Order Logical Reasoning Ability Through {L}ogic{NLI}",
author = "Tian, Jidong and
Li, Yitian and
Chen, Wenqing and
Xiao, Liqiang and
He, Hao and
Jin, Yaohui",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/LogicNLI.logical-fallacyhttps://github.com/causalNLP/logical-fallacy
@article{jin2022logical,
title={Logical fallacy detection},
author={Jin, Zhijing and Lalwani, Abhinav and Vaidhya, Tejas and Shen, Xiaoyu and Ding, Yiwen and Lyu, Zhiheng and Sachan, Mrinmaya and Mihalcea, Rada and Sch{\"o}lkopf, Bernhard},
journal={arXiv preprint arXiv:2202.13758},
year={2022}
}
Chinese-Logic-Multiple-Choicelogical-entailmenthttps://github.com/google-deepmind/logical-entailment-dataset
@inproceedings{
evans2018can,
title={Can Neural Networks Understand Logical Entailment?},
author={Richard Evans and David Saxton and David Amos and Pushmeet Kohli and Edward Grefenstette},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=SkZxCk-0Z},
}
multi-zebra-logic
Dataset Card for the MultiZebraLogic dataset
This dataset includes zebra puzzles in 39 European and 5 non-European languages and in two sizes: 2x3 and 4x5. It can be used for evaluating logical reasoning ability.
The data has been generated using the code in this repo.
Dataset Details
Dataset Description
Zebra puzzles are a type of constraint satisfaction problem. They describe a number of objects, N_objects, that each have attributes… See the full description on the dataset page: https://huggingface.co/datasets/alexandrainst/multi-zebra-logic.logical-reasoningLogical-Reasoning-1500-DataSWE-Star
SWE-Star
Introduction
SWE-Star is a family of language models based on the Qwen2.5-Coder family and trained on the SWE-Star dataset. The dataset contains approximately 250k agentic coding trajectories distilled from Devstral-2-Small using SWE-Smith tasks.
The complete data generation, training, and evaluation pipeline is openly available in our GitHub repository, enabling anyone to reproduce our results.
Additional details are available in our blog posts.… See the full description on the dataset page: https://huggingface.co/datasets/LogicStar/SWE-Star.Video-MME-v2-logic-only-replacedLogicalReasoning-hard-v2logical-reasoning-qa-dataset
Dataset Card for "logical-reasoning-qa-dataset"
More Information needed
LogicalReasoning-hard-v1LogicInference_OA
Dataset Card for "LogicInference_OA"
This is an re-produce of the dataset from LogicInference Dataset in paper: https://openreview.net/pdf?id=HAGeIS_Lcg9.
The github page of LogicInference Dataset: https://github.com/google-research/google-research/tree/master/logic_inference_dataset.
This dataset is aimed to offer more dataset for Open Assistant project, depending on their demands, there three columns: INSTRUCTION, RESPONSE, SOURCE.
The results in this dataset is a little different… See the full description on the dataset page: https://huggingface.co/datasets/KK04/LogicInference_OA.logiclm_bookcorpus_dataset
Dataset Card for "logiclm_bookcorpus_dataset"
More Information needed
task110_logic2text_sentence_generation
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task110_logic2text_sentence_generation
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task110_logic2text_sentence_generation.logicnlg
LogicNLG Dataset
See the official wenhuchen/LogicNLG release on GitHub.
LogicalReasoning-hard-v3first_rag_db_manuel_config_trial
Atlas Hospital Türkçe Medikal RAG Deneyi
Bu depo, bir metni parçalama, parçaları gömme (embedding), ChromaDB'ye kaydetme ve benzerlik eşiğiyle cevaplanabilirlik kararı verme adımlarını uçtan uca göstermek için hazırlanmış bir ödev çalışmasıdır.
Kaynak veri, umutertugrul/turkish-hospital-medical-articles veri setindeki Atlas Hospital bölümüdür. Ham dosyada 130 makale bulunur; metne göre yinelenen iki kayıt çıkarıldığında 128 benzersiz makale işlenir.
Bu çalışma eğitim amaçlıdır.… See the full description on the dataset page: https://huggingface.co/datasets/logicBombExe/first_rag_db_manuel_config_trial.task210_logic2text_structured_text_generation
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task210_logic2text_structured_text_generation
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task210_logic2text_structured_text_generation.logical-reasoning-training-pool
Logical reasoning training pool
Public logical-reasoning problems with checkable answers, from two datasets whose licences allow
commercial use, read at the pinned revisions named below and laid out twice. Every problem is an
entailment problem: a block of premises, one conclusion, and whether the premises make the
conclusion true, false or neither. Train on either layer or on both.
pool.jsonl
Every source rewritten into one shape, 340951 rows, one JSON object per… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/logical-reasoning-training-pool.logic-grid-puzzles-training-pool
Logic grid puzzles training pool
Logic grid puzzles: a row of positions, a handful of attributes with one value per position, and a
list of clues that together admit exactly one arrangement. Two sets drawn for this pool by
generators run here under the seeds recorded below, and two public datasets read at the pinned
revisions named below, laid out twice. Train on either layer or on both.
pool.jsonl
Every source rewritten into one shape, 390945 rows, one JSON… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/logic-grid-puzzles-training-pool.task211_logic2text_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task211_logic2text_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task211_logic2text_classification.LOGIC-701
LOGIC-701 Benchmark
This is a synthetic and filtered dataset for benchmarking large language models (LLMs). It consists of 701 medium and hard logic puzzles with solutions on 10 distinct topics.
A feature of the dataset is that it tests exclusively logical/reasoning abilities, offering only 5 answer options. There are no or very few tasks in the dataset that require external knowledge about events, people, facts, etc.
Languages
This benchmark is also part of an… See the full description on the dataset page: https://huggingface.co/datasets/hivaze/LOGIC-701.corpus-logica-10btask717_mmmlu_answer_generation_logical_fallacies
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task717_mmmlu_answer_generation_logical_fallacies
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task717_mmmlu_answer_generation_logical_fallacies.judged_logic_completionsLogicHaystacks
Evaluation code:
def parse(x):
if '<answer>' in x and '</answer>' in x:
start = x.find('<answer>') + len('<answer>')
end = x.find('</answer>')
x = x[start:end]
lines = [i.lstrip('L').strip() for i in x.strip().strip('.').split(',')]
return [int(i) for i in lines if i.isnumeric()]
def jaccard(list1, list2):
intersection = len(list(set(list1).intersection(list2)))
union = (len(set(list1)) + len(set(list2))) - intersection
return… See the full description on the dataset page: https://huggingface.co/datasets/sileod/LogicHaystacks.formal-logic-simple-order-multi-token-dynamic-objects-paired-relationship-0-100000