CoolFace
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squad_v2

rajpurkar /squad_v2 Dataset Card for SQuAD 2.0 Dataset Summary Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD 2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad_v2.textquestion-answering100K<n<1M263 likes98k downloads3y agoHugging Facelighteval /squad_v2text100K<n<1M0 likes1.1k downloads1y agoHugging FaceGEM /squad_v2 SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.textother100K<n<1M4 likes480 downloads4y agoHugging Facegpantaz /squadv2trainimage100K<n<1M0 likes254 downloads2y agoHugging FaceKorQuAD /squad_kor_v2KorQuAD 2.0 is a Korean question and answering dataset consisting of a total of 100,000+ pairs. There are three major differences from KorQuAD 1.0, which is the standard Korean Q & A data. The first is that a given document is a whole Wikipedia page, not just one or two paragraphs. Second, because the document also contains tables and lists, it is necessary to understand the document structured with HTML tags. Finally, the answer can be a long text covering not only word or phrase units, but paragraphs, tables, and lists. As a baseline model, BERT Multilingual is used, released by Google as an open source. It shows 46.0% F1 score, a very low score compared to 85.7% of the human F1 score. It indicates that this data is a challenging task. Additionally, we increased the performance by no-answer data augmentation. Through the distribution of this data, we intend to extend the limit of MRC that was limited to plain text to real world tasks of various lengths and formats.question-answering10K<n<100K18 likes235 downloads3y agoHugging Facebowang0911 /squad-v2-dutch SQuAD v2 Dutch (MTEB retrieval format) Dutch general-knowledge (Wikipedia) retrieval task. Given a Dutch question, retrieve the relevant Wikipedia passage from the corpus. Built from the validation split, answerable questions only. Reformatted into MTEB retrieval format from yhavinga/squad_v2_dutch, a machine translation of SQuAD v2. License: CC BY-SA 4.0. text10K<n<100K0 likes215 downloads3mo agoHugging Face