[B! dataset] Nyohoã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

Nyoho id:Nyoho

datasetã«é–¢ã™ã‚‹Nyohoã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (24)

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JSUT (Japanese speech corpus of Saruwatari Lab, University of Tokyo) - Shinnosuke Takamichi
JSUT (Japanese speech corpus of Saruwatari-lab., University of Tokyo) The JSUT Collection is Japanese speech corpora connecting speech, song, and audio events. The JSUT corpus is a part of the JSUT Collection. JSUT ã‚³ãƒ¬ã‚¯ã‚·ãƒ§ãƒ³ã¯ï¼Œå£°ãƒ»æŒãƒ»éŸ³å£°æ¨¡å€£ã‚’ã¤ãªã’ã‚‹ãŸã‚ã®éŸ³å£°ã‚³ãƒ¼ãƒ‘ã‚¹ã§ã™ï¼Žã“ã®JSUT ã‚³ãƒ¼ãƒ‘ã‚¹ã¯ï¼ŒJSUT ã‚³ãƒ¬ã‚¯ã‚·ãƒ§ãƒ³ã®ä¸€éƒ¨ã§ã™ï¼Ž This corpus consists of Japanese text (transcription) and reading-style audio. The audio data is sampled at 48kHz and rec
Nyoho 2022/10/12
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dataset
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Papers with Code - Machine Learning Datasets
CIFAR-10 (Canadian Institute for Advanced Research, 10 classes) The CIFAR-10 dataset (Canadian Institute for Advanced Research, 10 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The images are labelled with one of 10 mutually exclusive classes: airplane, automobile (but not truck or pickup truck), bird, cat, deer, dog, frog, horse, ship, and truck (but no
Nyoho 2021/02/04
dataset

æ©Ÿæ¢°å¦ç¿’
ãƒªãƒ³ã‚¯
GitHub - mantra-inc/open-mantra-dataset
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Nyoho 2020/12/19
dataset
ãƒªãƒ³ã‚¯
Thomas Wolf on Twitter: "Surviving every AI wave, two kernels have consistently been the beating hearts of Natural Language Processing: Datâ€¦ https://t.co/heOt3pzUOA"
Nyoho 2020/05/19
ã™ã”ã„

nlp

dataset
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CCMatrix: A billion-scale bitext dataset for training translation models
CCMatrix: A billion-scale bitext dataset for training translation models What it is:CCMatrix is the largest dataset of high-quality, web-based bitexts for training translation models. With more than 4.5 billion parallel sentences in 576 language pairs pulled from snapshots of the CommonCrawl public dataset, CCMatrix is more than 50 times larger than the WikiMatrix corpus that we shared last year.
Nyoho 2020/02/08
dataset

NLP
ãƒªãƒ³ã‚¯
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ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ ã€Žæ—¥æœ¬å¤å…¸ç±ããšã—å—ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã€ï¼ˆå›½æ–‡å¦ç ”ç©¶è³‡æ–™é¤¨ã»ã‹æ‰€è”µï¼æƒ…å ±ãƒ»ã‚·ã‚¹ãƒ†ãƒ ç ”ç©¶æ©Ÿæ§‹ ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ã‚¹å…±åŒåˆ©ç”¨åŸºç›¤æ–½è¨ äººæ–‡å¦ã‚ªãƒ¼ãƒ—ãƒ³ãƒ‡ãƒ¼ã‚¿å…±åŒåˆ©ç”¨ã‚»ãƒ³ã‚¿ãƒ¼åŠ å·¥ï¼‰ã¯ã‚¯ãƒªã‚¨ã‚¤ãƒ†ã‚£ãƒ–ãƒ»ã‚³ãƒ¢ãƒ³ã‚º è¡¨ç¤º - ç¶™æ‰¿ 4.0 å›½éš› ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ï¼ˆCC BY-SAï¼‰ã®ä¸‹ã«æä¾›ã•ã‚Œã¦ã„ã¾ã™ã€‚ ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆå…¨ä½“ã‚’ã”åˆ©ç”¨ã®éš›ã«ã¯ã€ä¾‹ãˆã°ä»¥ä¸‹ã®ã‚ˆã†ãªè¡¨ç¤ºã‚’ãŠé¡˜ã„ã—ã¾ã™ã€‚å€‹åˆ¥ã®å¤å…¸ç±ã®ã¿ã‚’ã”åˆ©ç”¨ã®å ´åˆã«ã¯ã€ãã‚Œãžã‚Œã®ãƒšãƒ¼ã‚¸ã‚’ã”è¦§ä¸‹ã•ã„ã€‚ ã€Žæ—¥æœ¬å¤å…¸ç±ããšã—å—ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã€ï¼ˆå›½æ–‡ç ”ã»ã‹æ‰€è”µï¼CODHåŠ å·¥ï¼‰ doi:10.20676/00000340 å¯èƒ½ãªå ´åˆã¯ã€ãƒ‡ãƒ¼ã‚¿æä¾›å…ƒã§ã‚ã‚‹ROIS-DSäººæ–‡å¦ã‚ªãƒ¼ãƒ—ãƒ³ãƒ‡ãƒ¼ã‚¿å…±åŒåˆ©ç”¨ã‚»ãƒ³ã‚¿ãƒ¼ã¸ã®ãƒªãƒ³ã‚¯ã‚’ãŠé¡˜ã„ã—ã¾ã™ã€‚ æä¾›ï¼šROIS-DSäººæ–‡å¦ã‚ªãƒ¼ãƒ—ãƒ³ãƒ‡ãƒ¼ã‚¿å…±åŒåˆ©ç”¨ã‚»ãƒ³ã‚¿ãƒ¼ ãƒ‡ãƒ¼ã‚¿æä¾›æ–¹æ³•ãƒ»æ³¨æ„äº‹é … æ›¸ç±ã”ã¨ã«å—å½¢ã‚’ã¾ã¨ã‚ãŸZIPãƒ•ã‚¡ã‚¤ãƒ«ã€ãŠã‚ˆã³å…¨éƒ¨ã‚’ã¾ã¨ã‚ãŸZIP
Nyoho 2019/11/14
nlp

æ—¥æœ¬èªž

dataset
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MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims | CopeNLU
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims Abstract We contribute the largest publ icly available dataset of naturally occurring factual claims for the purpose of automatic claim verification. It is collected from 26 fact checking websites in English, paired with textual sources and rich metadata, and labelled for veracity by human expert journalists. We p
Nyoho 2019/09/14
ãƒ•ã‚¡ã‚¯ãƒˆãƒã‚§ãƒƒã‚¯ã®ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã€‚ç´ æ™´ã‚‰ã—ã™ãŽã‚‹ã€‚

æ©Ÿæ¢°å¦ç¿’

dataset
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Groove MIDI Dataset
The Groove MIDI Dataset (GMD) is composed of 13.6 hours of aligned MIDI and (synthesized) audio of human-performed, tempo-aligned expressive drumming. The dataset contains 1,150 MIDI files and over 22,000 measures of drumming. Contents License Dataset MIDI Data Format Drum Mapping Control Changes Download TensorFlow Dataset How to Cite Acknowledgements License The dataset is made available by Goog
Nyoho 2019/05/24
dataset

music
ãƒªãƒ³ã‚¯
Dataset list - A list of datasets and annotation tools
A multitask benchmarking framework comprising complementary data modalities at a city-scale size, registered across different representations, and enriched with human and machine generated annotations. 27,745 high-resolution 360Â° images with human-curated annotations, 3D point clouds from: aerial and street-level LIDAR, Structure-from-Motion and Multiview-Stereo reconstructions, geo-anchored based
Nyoho 2019/03/30
dataset
ãƒªãƒ³ã‚¯
Box
Nyoho 2019/02/17
åŒ»ç™‚ç”»åƒãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆ

dataset
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GitHub - festvox/datasets-CMU_Wilderness: CMU Wilderness Multilingual Speech Dataset
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Nyoho 2018/12/20
dataset
ãƒªãƒ³ã‚¯
KMNISTãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆ | äººæ–‡å¦ã‚ªãƒ¼ãƒ—ãƒ³ãƒ‡ãƒ¼ã‚¿å…±åŒåˆ©ç”¨ã‚»ãƒ³ã‚¿ãƒ¼
KMNISTã¨ã¯ã€æ©Ÿæ¢°å¦ç¿’ç ”ç©¶ã§è‘—åãªMNISTãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆäº’æ›ã®ããšã—å—ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã¨ã—ã¦ã€ æ—¥æœ¬å¤å…¸ç±ããšã—å—ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‹ã‚‰æ´¾ç”Ÿã—ãŸãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã§ã™ã€‚MNISTãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã«å¯¾å¿œã—ãŸæ©Ÿæ¢°å¦ç¿’ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã§ã‚ã‚Œã°ã€è¨å®šã‚’å¤‰æ›´ã™ã‚‹ã ã‘ã§æ°—è»½ã«KMNISTã‚’è©¦ã™ã“ã¨ãŒã§ãã¾ã™ã€‚ç›®çš„ã«å¿œã˜ã¦ã€Kuzushiji-MNISTã€Kuzushiji-49ã€Kuzushiji-Kanjiã®3ç¨®é¡žã®ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‚’ã”åˆ©ç”¨ä¸‹ã•ã„ã€‚ KMNISTãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã¯ã€ROIS-DSäººæ–‡å¦ã‚ªãƒ¼ãƒ—ãƒ³ãƒ‡ãƒ¼ã‚¿å…±åŒåˆ©ç”¨ã‚»ãƒ³ã‚¿ãƒ¼ï¼ˆCODHï¼‰ãŒä½œæˆã—ãŸã‚‚ã®ã§ã‚ã‚Šã€å›½æ–‡å¦ç ”ç©¶è³‡æ–™é¤¨ãŒä½œæˆã—ãŸæ—¥æœ¬å¤å…¸ç±ããšã—å—ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‚’å…ƒã«ã—ã¦ã„ã¾ã™ã€‚ã”åˆ©ç”¨ã®éš›ã«ã¯ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ã‚’ã”ç¢ºèªä¸‹ã•ã„ã€‚ GitHub: Repository for Kuzushiji-MNIST, Kuzushiji-49, and Kuzushiji-Kanji Th
Nyoho 2018/12/14
ç´ æ™´ã‚‰ã—ã™ãŽã‚‹ã€‚

dataset
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fastMRI Dataset
âš¡ New Cardiac MRI Reconstruction Challenge 2024âš¡ Please consider participating in the CMRxRecon challenge, which is part of the MICCAI 2024 conference. More information can be found at:Â cmrxrecon.github.io (The CMRxRecon challenge is not organized by or affiliated with New York University or NYU Langone Health) About Us Here at the Center for Advanced Imaging Innovation and Research (CAI2R), in t
Nyoho 2018/11/27
dataset
ãƒªãƒ³ã‚¯
ã‚»ãƒ³ã‚¿ãƒ¼è©¦é¨“XMLãƒ‡ãƒ¼ã‚¿ - ãƒãƒœãƒƒãƒˆã¯æ±å¤§ã«å…¥ã‚Œã‚‹ã‹ã€‚Todai Robot Project
ã€Œãƒãƒœãƒƒãƒˆã¯æ±å¤§ã«å…¥ã‚Œã‚‹ã‹ã€ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã§é–‹ç™ºã—ãŸå¤§å¦å…¥è©¦ã‚»ãƒ³ã‚¿ãƒ¼è©¦é¨“1990å¹´åº¦ï½ž2017å¹´åº¦ã®é›»ååŒ–å•é¡Œãƒ‡ãƒ¼ã‚¿ã‚’å…¬é–‹ã—ã¾ã™ã€‚ ã“ã®ãƒ‡ãƒ¼ã‚¿ã¯ã€æœ¬ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆã«ãŠã„ã¦ã€å·æ·»æ„›æ°ã‚’ä¸å¿ƒã¨ã—ã¦XMLã«ã‚ˆã‚‹æ§‹é€ åŒ–ä»•æ§˜ã‚’ç–å®šã—ã€ã‚¢ãƒŽãƒ†ãƒ¼ã‚·ãƒ§ãƒ³ã‚’å®Ÿæ–½ã—ãŸã‚‚ã®ã§ã™ã€‚ å…ƒãƒ‡ãƒ¼ã‚¿ã®è‘—ä½œæ¨©è€…ã§ã‚ã‚‹ç‹¬ç«‹è¡Œæ”¿æ³•äººå¤§å¦å…¥è©¦ã‚»ãƒ³ã‚¿ãƒ¼ã®ã”åŽšæ„ã«ã‚ˆã‚Šã€äººå·¥çŸ¥èƒ½ãªã©ã«é–¢ã™ã‚‹ç ”ç©¶ã®ãŸã‚ã®è³‡æºã¨ã—ã¦ä¸€èˆ¬ã«å…¬é–‹ã—ã¾ã™ã€‚ è©¦é¨“å•é¡Œãƒ‡ãƒ¼ã‚¿ é–‹ç™ºç”¨ãƒ‡ãƒ¼ã‚¿ï¼ˆdevelopment dataï¼‰ [1993å¹´ä»¥é™4å¹´ã”ã¨ã«2017å¹´åº¦ã¾ã§ã®ã‚»ãƒ³ã‚¿ãƒ¼è©¦é¨“ æœ¬è©¦é¨“ãŠã‚ˆã³è¿½è©¦é¨“] é–‹ç™ºãƒ†ã‚¹ãƒˆç”¨ãƒ‡ãƒ¼ã‚¿ï¼ˆdevelopment test dataï¼‰ [1991å¹´ä»¥é™4å¹´ã”ã¨ã«2015å¹´åº¦ã¾ã§ã®ã‚»ãƒ³ã‚¿ãƒ¼è©¦é¨“ æœ¬è©¦é¨“ãŠã‚ˆã³è¿½è©¦é¨“] æœ€çµ‚ãƒ†ã‚¹ãƒˆç”¨ãƒ‡ãƒ¼ã‚¿ï¼ˆfinal test dataï¼‰ [1990å¹´åº¦ä»¥é™2å¹´ã”ã¨ã«2016å¹´åº¦ã¾ã§ã®ã‚»ãƒ³ã‚¿ãƒ¼è©¦é¨“ æœ¬è©¦é¨“ãŠ
Nyoho 2018/11/06
ãŠãŠ! æ±ãƒãƒœãã‚“ã®ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆå…¬é–‹ã•ã‚ŒãŸã®ã‹

dataset
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[1810.03505] CINIC-10 is not ImageNet or CIFAR-10
Nyoho 2018/10/11
ImageNet ã¨ CIFAR-10 ã®é–“ãã‚‰ã„ã®ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‚‰ã—ã„ã€‚

dataset
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MELD
Multimodal EmotionLines Dataset (MELD) has been created by enhancing and extending EmotionLines dataset. MELD contains the same dialogue instances available in EmotionLines, but it also encompasses audio and visual modality along with text. MELD has more than 1400 dialogues and 13000 utterances from Friends TV series. Multiple speakers participated in the dialogues. Each utterance in a dialogue ha
Nyoho 2018/10/08
"MELD has more than 1300 dialogues and 13000 utterances from Friends TV series"

dataset
ãƒªãƒ³ã‚¯
Fashion-MNIST: Year In Review Â· Han Xiao Tech Blog - Neural Search & AI Engineering
Itâ€™s been one year since I released the Fashion-MNIST dataset in Aug. 2017. As I wrote in the README.md, Fashion-MNIST is intended to serve as a drop-in replacement for the original MNIST dataset, helping people to benchmark and understand machine learning algorithms. Over a year, I have seen a great deal of trends and developments in the machine learning field towards this direction. The dataset
Nyoho 2018/10/06
æ©Ÿæ¢°å¦ç¿’

dataset
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ã“ã‚Œã¯ä½¿ãˆã‚‹ãžãƒ¼ãƒ¼ã£ï¼æµ®ä¸–çµµã«æã‹ã‚ŒãŸ26ç¨®ã®å‹•ç‰©ãŒãƒ™ã‚¯ã‚¿ãƒ¼ç´ æã«ãªã£ã¦ç„¡æ–™ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰å…¬é–‹ | ã‚¢ãƒ¼ãƒˆ æ—¥æœ¬ç”»ãƒ»æµ®ä¸–çµµ - Japaaan
colissã•ã‚“ã§ã¨ã£ã¦ã‚‚ã‚¹ãƒ†ã‚ãªç´ æé›†ãŒç´¹ä»‹ã•ã‚Œã¦ã„ã¾ã—ãŸã€‚æ±Ÿæˆ¸æ™‚ä»£ã®æµ®ä¸–çµµã«æã‹ã‚ŒãŸå¤šç¨®ãªå‹•ç‰©ãŸã¡ãŒSVGã®ãƒ™ã‚¯ã‚¿ãƒ¼ç´ æã«ãªã£ã¦ç„¡æ–™ãƒ€ã‚¦ãƒ³ãƒãƒ¼ãƒ‰å…¬é–‹ã•ã‚Œã¦ã„ã‚‹ã‚“ã§ã™ã€‚ ã“ã‚Œã¯ã„ã„ã§ã™ãï¼æ—¥æœ¬ç”»ã‚„æµ®ä¸–çµµã®ä½œå“ãŒãƒ™ã‚¯ã‚¿ãƒ¼ç´ æã«ãªã£ã¦æä¾›ã•ã‚Œã¦ã„ã‚‹ã“ã¨ã¯ãªã‹ãªã‹ãªã„ã®ã§å¬‰ã—ã„ã€‚æµ®ä¸–çµµã®ãƒ†ã‚¤ã‚¹ãƒˆãŒãã®ã¾ã¾ç”ŸããŸãƒ‡ã‚¶ã‚¤ãƒ³ç´ æã€‚è±¡ã‚„éº’éºŸã¯ã¾ã•ã«å½“æ™‚ã®æµè¡Œã®ã‚¿ãƒƒãƒã€‚ ã—ã‹ã‚‚ç´ æã¯å•†ç”¨åˆ©ç”¨ã§ã‚‚ç„¡æ–™ã€‚æ”¹å¤‰ã—ã¦ä½¿ç”¨å¯èƒ½ã€‚ãƒ™ã‚¯ã‚¿ãƒ¼ç´ æãªã®ã§è‰²ã‚’å¤‰ãˆãŸã‚Šãƒ‘ã‚¹ã‚’å¤‰æ›´ã—ãŸã‚Šã™ã‚‹ã“ã¨ãŒã§ãã¾ã™ã€‚ ã“ã®ãƒ‡ãƒ¼ã‚¿ã®å…ƒã®ä½œå“ã¯æŒå·èŠ³è™Žï¼ˆã†ãŸãŒã‚ã‚ˆã—ã¨ã‚‰ï¼‰ã®ã€Œæ–°æ¿ã‘ã ã‚‚ã®å°½ã€ã§ã€ã‚¿ã‚¤ãƒˆãƒ«ã®é€šã‚Šã«æ§˜ã€…ãªå‹•ç‰©ãŒæã‹ã‚ŒãŸã€æ±Ÿæˆ¸æ™‚ä»£ã®ã„ã‚ã‚†ã‚‹ã€ŒãŠã‚‚ã¡ã‚ƒçµµã€ã¨è¨€ã‚ã‚Œã‚‹ã‚‚ã®ã§ã™ã€‚ å›½ç«‹å›½ä¼šå›³æ›¸é¤¨ãƒ‡ã‚¸ã‚¿ãƒ«ã‚³ãƒ¬ã‚¯ã‚·ãƒ§ãƒ³ã‚ˆã‚Š æ›¸èªŒID 023907912 æŒå·èŠ³è™Žã¨ã„ã†ã¨åäºŒæ”¯ã‚’ãƒŸãƒƒã‚¯ã‚¹ã—ãŸã€Œå®¶å†…å®‰å…¨ãƒ²å®ˆåäºŒæ”¯ä¹‹å›³ã€ãŒäººæ°—ã€‚
Nyoho 2018/09/23
dataset
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Making it easier to discover datasets
Nyoho 2018/09/06
ðŸŽ‰Google Dataset Search https://toolbox.google.com/datasetsearch

dataset
ãƒªãƒ³ã‚¯
CoQA: A Conversational Question Answering Challenge
CoQA is a large-scale dataset for building Conversational Question Answering systems. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation. CoQA is pronounced as coca . CoQA paper CoQA contains 127,000+ questions with answers collected from 8000+ conversations. Each conversati
Nyoho 2018/08/24
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dataset
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