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01google-research-datasets /qedQED, is a linguistically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. It is an expertannotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset.question-answering1K<n<10K4 likes669 downloads3y agoHugging Face02Helsinki-NLP /qed_amaraThe QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform. Developed by: Qatar Computing Research Institute, Arabic Language Technologies Group The QED Corpus is made public for RESEARCH purpose only. The corpus is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Copyright Qatar Computing Research Institute. All rights reserved. 225 languages, 9,291 bitexts total number of files: 271,558 total number of tokens: 371.76M total number of sentence fragments: 30.93Mtranslation100K<n<1M5 likes180 downloads3y agoHugging Face03Lots-of-LoRAs /task1690_qed_amara_translation Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1690_qed_amara_translation 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/task1690_qed_amara_translation.texttext-generation1K<n<10K0 likes112 downloads2y agoHugging Face04puttatidam /qed-vie-bitextmining qed-vie-bitextmining Deduplicated copy of kornwtp/qed-vie-bitextmining, part of the SEA-BED data-quality work. Source dataset: kornwtp/qed-vie-bitextmining Deduplicated on: 2026-09-04 Task type: bitext_mining Splits: train What changed Duplicate (source, target) pairs collapsed to one row, and per-side duplicate sources/targets collapsed so the split has a retrieval ceiling of 1.0 on both sides. Every removed row was an exact duplicate after normalization; no… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/qed-vie-bitextmining.text100K<n<1M0 likes98 downloads11d agoHugging Face05Lots-of-LoRAs /task769_qed_summarization Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task769_qed_summarization 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/task769_qed_summarization.texttext-generation1K<n<10K0 likes91 downloads2y agoHugging Face06puttatidam /qed-zsm-bitextmining qed-zsm-bitextmining Deduplicated copy of kornwtp/qed-zsm-bitextmining, part of the SEA-BED data-quality work. Source dataset: kornwtp/qed-zsm-bitextmining Deduplicated on: 2026-09-04 Task type: bitext_mining Splits: train What changed Duplicate (source, target) pairs collapsed to one row, and per-side duplicate sources/targets collapsed so the split has a retrieval ceiling of 1.0 on both sides. Every removed row was an exact duplicate after normalization; no… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/qed-zsm-bitextmining.text10K<n<100K0 likes85 downloads11d agoHugging Face07puttatidam /qed-ind-bitextmining qed-ind-bitextmining Deduplicated copy of kornwtp/qed-ind-bitextmining, part of the SEA-BED data-quality work. Source dataset: kornwtp/qed-ind-bitextmining Deduplicated on: 2026-09-04 Task type: bitext_mining Splits: train What changed Duplicate (source, target) pairs collapsed to one row, and per-side duplicate sources/targets collapsed so the split has a retrieval ceiling of 1.0 on both sides. Every removed row was an exact duplicate after normalization; no… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/qed-ind-bitextmining.text100K<n<1M0 likes84 downloads11d agoHugging Face08puttatidam /qed-tet-bitextmining qed-tet-bitextmining Deduplicated copy of kornwtp/qed-tet-bitextmining, part of the SEA-BED data-quality work. Source dataset: kornwtp/qed-tet-bitextmining Deduplicated on: 2026-09-04 Task type: bitext_mining Splits: train What changed Duplicate (source, target) pairs collapsed to one row, and per-side duplicate sources/targets collapsed so the split has a retrieval ceiling of 1.0 on both sides. Every removed row was an exact duplicate after normalization; no… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/qed-tet-bitextmining.textn<1K0 likes83 downloads11d agoHugging Face09puttatidam /qed-tam-bitextmining qed-tam-bitextmining Deduplicated copy of kornwtp/qed-tam-bitextmining, part of the SEA-BED data-quality work. Source dataset: kornwtp/qed-tam-bitextmining Deduplicated on: 2026-09-04 Task type: bitext_mining Splits: train What changed Duplicate (source, target) pairs collapsed to one row, and per-side duplicate sources/targets collapsed so the split has a retrieval ceiling of 1.0 on both sides. Every removed row was an exact duplicate after normalization; no… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/qed-tam-bitextmining.text1K<n<10K0 likes82 downloads11d agoHugging Face10puttatidam /qed-mya-bitextmining qed-mya-bitextmining Deduplicated copy of kornwtp/qed-mya-bitextmining, part of the SEA-BED data-quality work. Source dataset: kornwtp/qed-mya-bitextmining Deduplicated on: 2026-09-04 Task type: bitext_mining Splits: train What changed Duplicate (source, target) pairs collapsed to one row, and per-side duplicate sources/targets collapsed so the split has a retrieval ceiling of 1.0 on both sides. Every removed row was an exact duplicate after normalization; no… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/qed-mya-bitextmining.text1K<n<10K0 likes81 downloads11d agoHugging Face11puttatidam /qed-tha-bitextmining qed-tha-bitextmining Deduplicated copy of kornwtp/qed-tha-bitextmining, part of the SEA-BED data-quality work. Source dataset: kornwtp/qed-tha-bitextmining Deduplicated on: 2026-09-04 Task type: bitext_mining Splits: train What changed Duplicate (source, target) pairs collapsed to one row, and per-side duplicate sources/targets collapsed so the split has a retrieval ceiling of 1.0 on both sides. Every removed row was an exact duplicate after normalization; no… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/qed-tha-bitextmining.text100K<n<1M0 likes81 downloads11d agoHugging Face12puttatidam /qed-khm-bitextmining qed-khm-bitextmining Deduplicated copy of kornwtp/qed-khm-bitextmining, part of the SEA-BED data-quality work. Source dataset: kornwtp/qed-khm-bitextmining Deduplicated on: 2026-09-04 Task type: bitext_mining Splits: train What changed Duplicate (source, target) pairs collapsed to one row, and per-side duplicate sources/targets collapsed so the split has a retrieval ceiling of 1.0 on both sides. Every removed row was an exact duplicate after normalization; no… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/qed-khm-bitextmining.text1K<n<10K0 likes78 downloads11d agoHugging Face13puttatidam /qed_fil_bitextmining qed_fil_bitextmining Deduplicated copy of kornwtp/qed_fil_bitextmining, part of the SEA-BED data-quality work. Source dataset: kornwtp/qed_fil_bitextmining Deduplicated on: 2026-09-04 Task type: bitext_mining Splits: train What changed Duplicate (source, target) pairs collapsed to one row, and per-side duplicate sources/targets collapsed so the split has a retrieval ceiling of 1.0 on both sides. Every removed row was an exact duplicate after normalization; no… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/qed_fil_bitextmining.text1K<n<10K0 likes77 downloads11d agoHugging Face14puttatidam /qed-lao-bitextmining qed-lao-bitextmining Deduplicated copy of kornwtp/qed-lao-bitextmining, part of the SEA-BED data-quality work. Source dataset: kornwtp/qed-lao-bitextmining Deduplicated on: 2026-09-04 Task type: bitext_mining Splits: train What changed Duplicate (source, target) pairs collapsed to one row, and per-side duplicate sources/targets collapsed so the split has a retrieval ceiling of 1.0 on both sides. Every removed row was an exact duplicate after normalization; no… See the full description on the dataset page: https://huggingface.co/datasets/puttatidam/qed-lao-bitextmining.textn<1K0 likes75 downloads11d agoHugging Face15Derify /safe_druglike_QED_33m Druglike QED 36M - SAFE Dataset This dataset is derived from Druglike molecule datasets for drug discovery. SAFE (sequential attachment-based fragment embedding) representations were generated using safe-mol (0.1.13). Dataset Overview: Source: SAFE representation: safe-mol (0.1.13) Total Entries: 33M SMILES textmask-generation10M<n<100M0 likes68 downloads2y agoHugging Face16ArchSid /QE-DA-datasets0 likes65 downloads2y agoHugging Face17Derify /augmented_canonical_druglike_QED_43m Druglike QED 43M - Augmented SMILES Dataset This dataset is derived from the Druglike molecule datasets for drug discovery dataset and has been canonicalized using RDKit (2024.9.4) to ensure structural consistency. To enhance molecular diversity, 33% of the dataset was randomly sampled and augmented using RDKit’s Chem.MolToRandomSmilesVect function, following an approach similar to NVIDIA's molmim method for SMILES augmentation. Dataset Overview: Source:… See the full description on the dataset page: https://huggingface.co/datasets/Derify/augmented_canonical_druglike_QED_43m.textfeature-extraction10M<n<100M1 likes61 downloads1y agoHugging Face18Derify /augmented_canonical_druglike_QED_Pfizer_15m Druglike QED Pfizer 15M - Augmented SMILES Dataset This dataset is derived from the Druglike molecule datasets for drug discovery dataset and has been canonicalized using RDKit (2024.9.4) to ensure structural consistency. To enhance molecular diversity, 33% of the dataset was randomly sampled and augmented using RDKit’s Chem.MolToRandomSmilesVect function, following an approach similar to NVIDIA's molmim method for SMILES augmentation. Dataset Overview: Source:… See the full description on the dataset page: https://huggingface.co/datasets/Derify/augmented_canonical_druglike_QED_Pfizer_15m.textfeature-extraction10M<n<100M1 likes54 downloads1y agoHugging Face19powerpuffpomelo /qe_data_yanym0 likes52 downloads3y agoHugging Face20SEACrowd /qedQED - The QCRI Educational Domain Corpus (formerly QCRI AMARA Corpus) is an open multilingual collection of subtitles for educational videos and lectures collaboratively transcribed and translated over the AMARA web-based platform. It's developed by Qatar Computing Research Institute, Arabic Language Technologies Group. Along with English, it covers multiple SEA languages, such as vie (Vietnamese), mya (Burnmese), jav (Javanese), id (Indonesia), tha (Thai), tl (Tagalog), ms (Malaysia).0 likes40 downloads2y agoHugging Face21hgissbkh /qedtext1K<n<10K0 likes29 downloads1y agoHugging Face22kornwtp /qed-ind-bitextminingref: https://opus.nlpl.eu text100K<n<1M0 likes27 downloads2y agoHugging Face23Lots-of-LoRAs /task1689_qed_amara_translation Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1689_qed_amara_translation 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/task1689_qed_amara_translation.texttext-generation1K<n<10K0 likes25 downloads2y agoHugging Face24kornwtp /qed-khm-bitextminingref: https://opus.nlpl.eu text1K<n<10K0 likes23 downloads2y agoHugging Face25Lots-of-LoRAs /task1691_qed_amara_translation Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1691_qed_amara_translation 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/task1691_qed_amara_translation.texttext-generation1K<n<10K0 likes23 downloads2y agoHugging Face26qqggez /QEDBenchgated QEDBench: A Benchmark for LLM Mathematical Proof Evaluation QEDBench is a challenging mathematical reasoning benchmark consisting of 272 proof-based problems spanning 10 mathematical domains, designed to evaluate LLMs on formal proof generation and evaluation. 🌐 Project Website For an interactive overview of the benchmark, pass-rate visualizations, and error analysis, please see the supplementary material. 👥 Team Principal Investigators: Quanquan C. Liu… See the full description on the dataset page: https://huggingface.co/datasets/qqggez/QEDBench.2 likes23 downloads7mo agoHugging Face27kornwtp /qed-tet-bitextminingtextn<1K0 likes21 downloads2y agoHugging Face28nguyen-brat /qedtext1K<n<10K0 likes20 downloads2y agoHugging Face29Alan123 /qa_dataset_with_qed_logp_mw_grpotext100K<n<1M0 likes19 downloads1y agoHugging Face30kornwtp /qed-lao-bitextminingtextn<1K0 likes17 downloads2y agoHugging Face

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