datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Chinese-Logic-Multiple-Choicecycic_multiplechoicehttps://colab.research.google.com/drive/16nyxZPS7-ZDFwp7tn_q72Jxyv0dzK1MP?usp=sharing
@article{Kejriwal2020DoFC,
title={Do Fine-tuned Commonsense Language Models Really Generalize?},
author={Mayank Kejriwal and Ke Shen},
journal={ArXiv},
year={2020},
volume={abs/2011.09159}
}
added for
@article{sileo2023tasksource,
title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
author={Sileo, Damien},
url=… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/cycic_multiplechoice.kvqa_multiple_choicelilac-TruthfulQA-MultipleChoice
lilac/TruthfulQA-MultipleChoice
This dataset is a Lilac processed dataset. Original dataset: https://huggingface.co/datasets/truthful_qa
To download the dataset to a local directory:
lilac download lilacai/lilac-TruthfulQA-MultipleChoice
or from python with:
ll.download("lilacai/lilac-TruthfulQA-MultipleChoice")
AfriMCQA-multiple-choice
Afri-MCQA multiple choice visual QA (MTEB)
Culturally grounded multiple choice questions about photographs, in 16 African
languages. Each row is a question, the photograph it asks about, its answer
options, and which of them is correct. Options are shuffled by a hash of the
question, because the source always lists the answer first.
Built from Atnafu/Afri-MCQA at revision 8b8c53d, cc-by-nc-4.0, using
the official dev split, the only one where the correct answer is labelled.… See the full description on the dataset page: https://huggingface.co/datasets/vnahata/AfriMCQA-multiple-choice.labeled-multiple-choice-explained-mistral-reasoninglabeled-multiple-choice-explained-falcon-reasoningtask750_aqua_multiple_choice_answering
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task750_aqua_multiple_choice_answering
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/task750_aqua_multiple_choice_answering.GenQA_multiple_choice
Dataset Card for "GenQA_multiple_choice"
More Information needed
legal-multiple-choice-v23GPP-QA-MultipleChoiceMultiple_Choice-Turkishgrammar_et_multiple_choiceTalTechNLP/grammar_et formatted as a multiple-choice problem.
labeled-multiple-choice-explained-falcon-tokenizedlegal-multiple-choice-v3Nemotron-Pretraining-Multiple-Choice-NL-train-shuffledkvqa_multiple_choicelabeled-multiple-choice-explainedlabeled-multiple-choice
Dataset Card for "labeled-multiple-choice"
More Information needed
cowese_abrev_multiplechoice
Dataset Card for "cowese_abrev_multiplechoice"
More Information needed
labeled-multiple-choice-explained-mistral-tokenizedPuzzleVQA-MultipleChoice-TranslatedPuzzleVQA-MultipleChoice-QAmultiple-choice-checkpoint-downloadsParsiNLU-multiple-choicepreprocessed_race_for_multiple_choice
Dataset Card for "preprocessed_race_for_multiple_choice"
More Information needed
vts-multiple-choice-109kdapo-multiple-choice-verificationmultiple_choice_bsardPuzzleVQA-MultipleChoice