Multilingual Machine Comprehension (MMC) is a Question-Answering (QA) sub-task that involves quoting the answer for a question from a given snippet, where the question and the snippet can be in different languages. Results on an extended version of the recently released XQuAD dataset, which we propose to use as the evaluation benchmark for future research.
데이터셋
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Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems.
This dataset is an extremely challenging set of over 2000+ original Visiting card/ID card images captured and crowdsourced from over 300+ urban and rural areas, where each image is manually reviewed and verified by computer vision professionals at Datacluster Labs. ### Dataset Features - Dataset size : 2000+ - Captured by : Over 150+ crowdsource contributors - Resolution : 100% images HD and above (1920x1080 and above) - Location : Captured with 300+ cities accross India - Diversity : Covers a wide variety of things such as reflective paper, different fonts, different color and type of cards, etc. - Device used : Captured using mobile phones in 2020-2021 - Usage : Visiting card detection, card edge detection, paper edge detection, ID card OCR, Hindi OCR, etc. ### Available Annotation formats COCO, YOLO, PASCAL-VOC, Tf-Record To download full datasets or to submit a request for your dataset needs, please ping us at sales@datacluster.ai Visit www.datacluster.ai to know more.Note: All the images are manually captured and verified by a large contributor base on DataCluster platform