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Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for building applications using machine learning.
Vietnamese Question Answering Dataset (UIT-ViQuAD) A large collection of Vietnamese questions for evaluating MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. 23,074 Question-answer pairs Question Answering 2020 [334] Nguyen et al.
Question answering systems in the context of [vague] machine reading applications have also been constructed in the medical domain, for instance related to [vague] Alzheimer's disease. [3] Open-domain question answering deals with questions about nearly anything and can only rely on general ontologies and world knowledge. Systems designed for ...
SQuAD (Stanford Question Answering Dataset [13]) v1.1 and v2.0; SWAG (Situations With Adversarial Generations [ 14 ] ). In the original paper, all parameters of BERT are finetuned, and recommended that, for downstream applications that are text classifications, the output token at the [CLS] input token is fed into a linear-softmax layer to ...
It consists of about 16,000 multiple-choice questions spanning 57 academic subjects including mathematics, philosophy, law, and medicine. It is one of the most commonly used benchmarks for comparing the capabilities of large language models, with over 100 million downloads as of July 2024.
The dataset was composed of BooksCorpus, and English Wikipedia, Giga5, ClueWeb 2012-B, and Common Crawl. It was trained on 512 TPU v3 chips, for 5.5 days. At the end of training, it still under-fitted the data, meaning it could have achieved lower loss with more training.
In 2021, the research contributions comprised German models and datasets for question answering and passage retrieval named GermanQuAD and GermanDPR, [7] a semantic answer similarity metric, [8] and an approach for multimodal retrieval of texts and tables to enable question answering on tabular data. [9]
The pretrain dataset is typically an unlabeled large corpus, such as The Pile. Tasks for pretraining and fine-tuning commonly include: language modeling [12] next-sentence prediction [12] question answering [3] reading comprehension; sentiment analysis [1] paraphrasing [1]