The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided.

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The ModelNet40 dataset contains synthetic object point clouds. As the most widely used benchmark for point cloud analysis, ModelNet40 is popular because of its various categories, clean shapes, well-constructed dataset, etc. The original ModelNet40 consists of 12,311 CAD-generated meshes in 40 categories (such as airplane, car, plant, lamp), of which 9,843 are used for training while the rest 2,468 are reserved for testing. The corresponding point cloud data points are uniformly sampled from the mesh surfaces, and then further preprocessed by moving to the origin and scaling into a unit sphere.

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Neural Radiance Fields (NeRF) is a method for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. The dataset contains three parts with the first 2 being synthetic renderings of objects called Diffuse Synthetic 360◦ and Realistic Synthetic 360◦ while the third is real images of complex scenes. Diffuse Synthetic 360◦ consists of four Lambertian objects with simple geometry. Each object is rendered at 512x512 pixels from viewpoints sampled on the upper hemisphere. Realistic Synthetic 360◦ consists of eight objects of complicated geometry and realistic non-Lambertian materials. Six of them are rendered from viewpoints sampled on the upper hemisphere and the two left are from viewpoints sampled on a full sphere with all of them at 800x800 pixels. The real images of complex scenes consist of 8 forward-facing scenes captured with a cellphone at a size of 1008x756 pixels.

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The Universal Dependencies (UD) project seeks to develop cross-linguistically consistent treebank annotation of morphology and syntax for multiple languages. The first version of the dataset was released in 2015 and consisted of 10 treebanks over 10 languages. Version 2.7 released in 2020 consists of 183 treebanks over 104 languages. The annotation consists of UPOS (universal part-of-speech tags), XPOS (language-specific part-of-speech tags), Feats (universal morphological features), Lemmas, dependency heads and universal dependency labels.

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The Cross-lingual Natural Language Inference (XNLI) corpus is the extension of the Multi-Genre NLI (MultiNLI) corpus to 15 languages. The dataset was created by manually translating the validation and test sets of MultiNLI into each of those 15 languages. The English training set was machine translated for all languages. The dataset is composed of 122k train, 2490 validation and 5010 test examples.

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OpenSubtitles is collection of multilingual parallel corpora. The dataset is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages.

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Common Voice is an audio dataset that consists of a unique MP3 and corresponding text file. There are 9,283 recorded hours in the dataset. The dataset also includes demographic metadata like age, sex, and accent. The dataset consists of 7,335 validated hours in 60 languages.

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MuST-C currently represents the largest publicly available multilingual corpus (one-to-many) for speech translation. It covers eight language directions, from English to German, Spanish, French, Italian, Dutch, Portuguese, Romanian and Russian. The corpus consists of audio, transcriptions and translations of English TED talks, and it comes with a predefined training, validation and test split.

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XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi. Consequently, the dataset is entirely parallel across 11 languages.

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MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA is highly parallel, with QA instances parallel between 4 different languages on average.

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Dataset of hate speech annotated on Internet forum posts in English at sentence-level. The source forum in Stormfront, a large online community of white nacionalists. A total of 10,568 sentence have been been extracted from Stormfront and classified as conveying hate speech or not.

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Total-Text is a text detection dataset that consists of 1,555 images with a variety of text types including horizontal, multi-oriented, and curved text instances. The training split and testing split have 1,255 images and 300 images, respectively.

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The MSRA-TD500 dataset is a text detection dataset that contains 300 training images and 200 test images. Text regions are arbitrarily orientated and annotated at sentence level. Different from the other datasets, it contains both English and Chinese text.

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PAWS-X contains 23,659 human translated PAWS evaluation pairs and 296,406 machine translated training pairs in six typologically distinct languages: French, Spanish, German, Chinese, Japanese, and Korean. All translated pairs are sourced from examples in PAWS-Wiki.

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The Microsoft Academic Graph is a heterogeneous graph containing scientific publication records, citation relationships between those publications, as well as authors, institutions, journals, conferences, and fields of study.

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ASPEC, Asian Scientific Paper Excerpt Corpus, is constructed by the Japan Science and Technology Agency (JST) in collaboration with the National Institute of Information and Communications Technology (NICT). It consists of a Japanese-English paper abstract corpus of 3M parallel sentences (ASPEC-JE) and a Japanese-Chinese paper excerpt corpus of 680K parallel sentences (ASPEC-JC). This corpus is one of the achievements of the Japanese-Chinese machine translation project which was run in Japan from 2006 to 2010.

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The Georgia Tech Egocentric Activities (GTEA) dataset contains seven types of daily activities such as making sandwich, tea, or coffee. Each activity is performed by four different people, thus totally 28 videos. For each video, there are about 20 fine-grained action instances such as take bread, pour ketchup, in approximately one minute.

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We release Douban Conversation Corpus, comprising a training data set, a development set and a test set for retrieval based chatbot. The statistics of Douban Conversation Corpus are shown in the following table.

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Math23K is a dataset created for math word problem solving, contains 23, 162 Chinese problems crawled from the Internet. Refer to our paper for more details: The dataset is originally introduced in the paper Deep Neural Solver for Math Word Problems. The original files are originally split into train/test split, while other research efforts (https://github.com/2003pro/Graph2Tree) perform the train/dev/test split.

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This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository.

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The shared task of CoNLL-2002 concerns language-independent named entity recognition. The types of named entities include: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task were offered training and test data for at least two languages. Information sources other than the training data might have been used in this shared task.

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The LUNA16 (LUng Nodule Analysis) dataset is a dataset for lung segmentation. It consists of 1,186 lung nodules annotated in 888 CT scans.

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OntoNotes 5.0 is a large corpus comprising various genres of text (news, conversational telephone speech, weblogs, usenet newsgroups, broadcast, talk shows) in three languages (English, Chinese, and Arabic) with structural information (syntax and predicate argument structure) and shallow semantics (word sense linked to an ontology and coreference).

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The CrowdPose dataset contains about 20,000 images and a total of 80,000 human poses with 14 labeled keypoints. The test set includes 8,000 images. The crowded images containing homes are extracted from MSCOCO, MPII and AI Challenger.

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DuReader is a large-scale open-domain Chinese machine reading comprehension dataset. The dataset consists of 200K questions, 420K answers and 1M documents. The questions and documents are based on Baidu Search and Baidu Zhidao. The answers are manually generated. The dataset additionally provides question type annotations – each question was manually annotated as either Entity, Description or YesNo and one of Fact or Opinion.

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CMRC is a dataset is annotated by human experts with near 20,000 questions as well as a challenging set which is composed of the questions that need reasoning over multiple clues.

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VATEX is multilingual, large, linguistically complex, and diverse dataset in terms of both video and natural language descriptions. It has two tasks for video-and-language research: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.

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The KVASIR Dataset was released as part of the medical multimedia challenge presented by MediaEval. It is based on images obtained from the GI tract via an endoscopy procedure. The dataset is composed of images that are annotated and verified by medical doctors, and captures 8 different classes. The classes are based on three anatomical landmarks (z-line, pylorus, cecum), three pathological findings (esophagitis, polyps, ulcerative colitis) and two other classes (dyed and lifted polyps, dyed resection margins) related to the polyp removal process. Overall, the dataset contains 8,000 endoscopic images, with 1,000 image examples per class.

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LCSTS is a large corpus of Chinese short text summarization dataset constructed from the Chinese microblogging website Sina Weibo, which is released to the public. This corpus consists of over 2 million real Chinese short texts with short summaries given by the author of each text. The authors also manually tagged the relevance of 10,666 short summaries with their corresponding short texts 10,666 short summaries with their corresponding short texts.

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MuPoTs-3D (Multi-person Pose estimation Test Set in 3D) is a dataset for pose estimation composed of more than 8,000 frames from 20 real-world scenes with up to three subjects. The poses are annotated with a 14-point skeleton model.

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The Weibo NER dataset is a Chinese Named Entity Recognition dataset drawn from the social media website Sina Weibo.

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DBP15k contains four language-specific KGs that are respectively extracted from English (En), Chinese (Zh), French (Fr) and Japanese (Ja) DBpedia, each of which contains around 65k-106k entities. Three sets of 15k alignment labels are constructed to align entities between each of the other three languages and En.

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Multilingual Document Classification Corpus (MLDoc) is a cross-lingual document classification dataset covering English, German, French, Spanish, Italian, Russian, Japanese and Chinese. It is a subset of the Reuters Corpus Volume 2 selected according to the following design choices:

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The Electricity Transformer Temperature (ETT) is a crucial indicator in the electric power long-term deployment. This dataset consists of 2 years data from two separated counties in China. To explore the granularity on the Long sequence time-series forecasting (LSTF) problem, different subsets are created, {ETTh1, ETTh2} for 1-hour-level and ETTm1 for 15-minutes-level. Each data point consists of the target value ”oil temperature” and 6 power load features. The train/val/test is 12/4/4 months.

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Delta Reading Comprehension Dataset (DRCD) is an open domain traditional Chinese machine reading comprehension (MRC) dataset. This dataset aimed to be a standard Chinese machine reading comprehension dataset, which can be a source dataset in transfer learning. The dataset contains 10,014 paragraphs from 2,108 Wikipedia articles and 30,000+ questions generated by annotators.

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CN-Celeb is a large-scale speaker recognition dataset collected `in the wild'. This dataset contains more than 130,000 utterances from 1,000 Chinese celebrities, and covers 11 different genres in real world.

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OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. The dataset used for training multilingual models such as BART incorporates 138 GB of text.

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Emotion-cause pair extraction (ECPE) aims to extract the potential pairs of emotions and corresponding causes in a document. This dataset consists of 1,945 Chinese documents from SINA NEWS website.

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Comprises 11 hand gesture categories from 29 subjects under 3 illumination conditions.

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WMT 2020 is a collection of datasets used in shared tasks of the Fifth Conference on Machine Translation. The conference builds on a series of annual workshops and conferences on Statistical Machine Translation.

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The BUCC mining task is a shared task on parallel sentence extraction from two monolingual corpora with a subset of them assumed to be parallel, and that has been available since 2016. For each language pair, the shared task provides a monolingual corpus for each language and a gold mapping list containing true translation pairs. These pairs are the ground truth. The task is to construct a list of translation pairs from the monolingual corpora. The constructed list is compared to the ground truth, and evaluated in terms of the F1 measure.

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OCNLI stands for Original Chinese Natural Language Inference. It is corpus for Chinese Natural Language Inference, collected following closely the procedures of MNLI, but with enhanced strategies aiming for more challenging inference pairs. No human/machine translation is used in creating the dataset, and thus the Chinese texts are original and not translated.

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WikiLingua includes ~770k article and summary pairs in 18 languages from WikiHow. Gold-standard article-summary alignments across languages are extracted by aligning the images that are used to describe each how-to step in an article.

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The SCUT-CTW1500 dataset contains 1,500 images: 1,000 for training and 500 for testing. In particular, it provides 10,751 cropped text instance images, including 3,530 with curved text. The images are manually harvested from the Internet, image libraries such as Google Open-Image, or phone cameras. The dataset contains a lot of horizontal and multi-oriented text.

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DialogRE is the first human-annotated dialogue-based relation extraction dataset, containing 1,788 dialogues originating from the complete transcripts of a famous American television situation comedy Friends. The are annotations for all occurrences of 36 possible relation types that exist between an argument pair in a dialogue. DialogRE is available in English and Chinese.

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THCHS-30 is a free Chinese speech database THCHS-30 that can be used to build a full-fledged Chinese speech recognition system.

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WikiAnn is a dataset for cross-lingual name tagging and linking based on Wikipedia articles in 295 languages.

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