Omitted documents with lengths <500 words or >500,000 words, or that were <90% English. Config description: Wikipedia dataset for mwl, parsed from 20200301 music recommendations: modeling music ratings with temporal dynamics and item taxonomy, Knowledge acquisition and explanation for multi-attribute decision making, MML inference of decision graphs with multi-way joins, "Quantifying comedy on YouTube: why the number of o's in your LOL matter", "Predicting Skytrax airport rankings from customer reviews", Split selection methods for classification trees, "The Reuters Corpus Volume 1-from Yesterday's News to Tomorrow's Language Resources", "Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization", "VRCA: a clustering algorithm for massive amount of texts", "Relationship and Entity Extraction Evaluation Dataset: Dstl/re3d", "News Headlines Dataset For Sarcasm Detection", The structure of information pathways in a social communication network, "Spam filtering using statistical data compression models", Contributions to the study of SMS spam filtering: new collection and results, A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization, Online Policy Adaptation for Ensemble Algorithms, https://github.com/sidooms/MovieTweetings, SeNTU: sentiment analysis of tweets by combining a rule-based classifier with supervised learning, Investigating homophily in online social networks, "Network-based statistical comparison of citation topology of bibliographic databases", On the automatic categorization of Arabic articles based on their political orientation, Prédictions d'activité dans les réseaux sociaux en ligne, SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT), Extracting Lexically Divergent Paraphrases from Twitter, "Real-Time Crisis Mapping of Natural Disasters Using Social Media", http://faculty.nps.edu/cmartell/NPSChat.htm, A Neural Network Approach to Context-Sensitive Generation of Conversational Responses, http://www.psych.ualberta.ca/~westburylab/downloads/usenetcorpus.download.html, http://www.comp.nus.edu.sg/entrepreneurship/innovation/osr/corpus/, https://www.reddit.com/r/datasets/comments/3bxlg7/i_have_every_publicly_available_reddit_comment/, The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructure Multi-Turn Dialogue Systems, Combining different summarization techniques for legal text, "Summarizing large text collection using topic modeling and clustering based on MapReduce framework", "MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text", "Building a large annotated corpus of English: The Penn Treebank", "Head-driven statistical models for natural language parsing", Feature extraction: foundations and applications, Syntactic annotations for the google books ngram corpus, "Generating Natural-Language Video Descriptions Using Text-Mined Knowledge", Personae: a Corpus for Author and Personality Prediction from Text, A case study of sockpuppet detection in wikipedia, Agglomeration and elimination of terms for dimensionality reduction, From group to individual labels using deep features, A large annotated corpus for learning natural language inference, T-REx: A Large Scale Alignment of Natural Language with Knowledge Base Triples, "Computers Are Learning to Read—But They're Still Not So Smart", The Zero Resource Speech Challenge 2015: Proposed Approaches and Results, Automatic detection of expressed emotion in Parkinson's disease, "Optimization techniques for semi-supervised support vector machines", "Accurate telemonitoring of Parkinson's disease progression by noninvasive speech tests", Predicting the geographical origin of music, "Unsupervised learning of sparse features for scalable audio classification", "Carpediem: Optimizing the viterbi algorithm and applications to supervised sequential learning", "Classification Active Learning Based on Mutual Information", A dataset and taxonomy for urban sound research, International Conference on Acoustics, Speech, and Signal Processing, "Watch out, birders: Artificial intelligence has learned to spot birds from their songs", http://www.caida.org/data/passive/witty_worm_dataset.xml, Optimal worm-scanning method using vulnerable-host distributions, Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time, "A principal components approach to combining regression estimates", Mean Mutual Information of Probabilistic Wi-Fi Localization, Data Acquisition and Signal Analysis from Measured Motor Currents for Defect Detection in Electromechanical Drive Systems, Wearable computing: Accelerometers’ data classification of body postures and movements, "Augmenting the senses: a review on sensor-based learning support", Gesture unit segmentation using support vector machines: segmenting gestures from rest positions, "A survey of applications and human motion recognition with Microsoft Kinect", Action classification of 3d human models using dynamic ANNs for mobile robot surveillance, 3D human action recognition and style transformation using resilient backpropagation neural networks. Config description: Wikipedia dataset for sw, parsed from 20200301 dump. Config description: Wikipedia dataset for gu, parsed from 20200301 dump. dump. ", Bhattacharya, Sourav, and Nicholas D. Lane. Datasets containing electric signal information requiring some sort of Signal processing for further analysis. ", Amberg, Brian, Reinhard Knothe, and Thomas Vetter. A large crowd-sourced dataset for developing natural language interfaces for relational databases. Collected for experiments in Authorship Attribution and Personality Prediction. dump. Weekly data of stocks from the first and second quarters of 2011. Config description: Wikipedia dataset for rm, parsed from 20190301 dump. The datasets are built from the Wikipedia dump Based on BSDS300. [18] It became officially available in Sep 2019. Config description: Wikipedia dataset for pnt, parsed from 20200301 Data from Twitter and Tom's Hardware. conversion from tf.Tensor to np.array. ", Anand, Pranav, et al. A Large set of images listed as having CC BY 2.0 license with image-level labels and bounding boxes spanning thousands of classes. Features of concrete given such as fly ash, water, etc. Google also released Colaboratory, which is a TensorFlow Jupyter notebook environment that requires no setup to use. 18 different types of physical activities performed by 9 subjects wearing 3 IMUs. dump. Config description: Wikipedia dataset for sm, parsed from 20190301 dump. "Inductive knowledge acquisition: a case study. Contains all bids, bidderID, bid times, and opening prices. dump. they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. "Arabic sentiment analysis: Corpus-based and lexicon-based. 20200301 dump. Config description: Wikipedia dataset for et, parsed from 20200301 dump. ", Bisgin, Halil, Nitin Agarwal, and Xiaowei Xu. Config description: Wikipedia dataset for wa, parsed from 20190301 dump. Credit card applications either accepted or rejected and attributes about the application. Let us suppose that you have access to a corpus of text. 849 images taken in 75 different scenes. dump. Here is how: the extraterrestrial ship from deep space enters the solar system and abducts a boater on earth . "Believe Me-We Can Do This! It contains color images in dynamic marine environments, each image may contain one or multiple targets in different weather and illumination conditions. dump. Trip data for yellow and green taxis in New York City. Camera shake has been removed from trajectories. Config description: Wikipedia dataset for tt, parsed from 20200301 dump. For details on how to pre-process English Wikipedia to obtain sentences, look at the github code. Config description: Wikipedia dataset for ia, parsed from 20200301 dump. Posts from age-specific online chat rooms. Images with multiple objects. Config description: Wikipedia dataset for ik, parsed from 20190301 dump. Config description: Wikipedia dataset for iu, parsed from 20190301 dump. Some examples require MNIST dataset for training and testing. Config description: Wikipedia dataset for br, parsed from 20200301 dump. dump. Config description: Wikipedia dataset for jbo, parsed from 20200301 Config description: Wikipedia dataset for nah, parsed from 20200301 ", Clark, David, Zoltan Schreter, and Anthony Adams. Google unveiled a powerful model in March 2018 called Universal Sentence Encoder (USE). Have a look at the dataset catalog dump. TensorFlow v1. Config description: Wikipedia dataset for bs, parsed from 20190301 dump. Config description: Wikipedia dataset for srn, parsed from 20190301 The duration of each video is about 85 seconds (about 345 frames). Config description: Wikipedia dataset for frp, parsed from 20200301 Config description: Wikipedia dataset for gn, parsed from 20190301 dump. Monte Carlo generated high-energy gamma particle events. tf.data guide TensorFlow computations are expressed as stateful dataflow graphs. [1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. A multilingual collection of short excerpts of journalistic texts in similar languages and dialects. Like CIFAR-10, above, but 100 classes of objects are given. dump. Data about frequency, angle of attack, etc., are given. Return the sentences corresponding to the original indexes. ", Kossinets, Gueorgi, Jon Kleinberg, and Duncan Watts. is applied on those files. Config description: Wikipedia dataset for koi, parsed from 20200301 Example: when using train[:50%] with Config description: Wikipedia dataset for hy, parsed from 20200301 dump. Natural language processing, machine comprehension. dump. Config description: Wikipedia dataset for kj, parsed from 20190301 dump. ", Sztyler, Timo, and Heiner Stuckenschmidt. [32] Other major changes included removal of old libraries, cross-compatibility between trained models on different versions of TensorFlow, and significant improvements to the performance on GPU. By default, TFDS auto-caches datasets which satisfy the following constraints: It is possible to opt out of auto-caching by passing try_autocaching=False to Config description: Wikipedia dataset for ms, parsed from 20190301 dump. dump. Config description: Wikipedia dataset for kv, parsed from 20190301 dump. Mohammad, Rami M., Fadi Thabtah, and Lee McCluskey. Config description: Wikipedia dataset for ne, parsed from 20190301 dump. Classes labelled, training, validation, test set splits created. 735 answer sheets and 33,540 answer boxes, Development of multiple choice test assessment systems. Location of facial features extracted. Config description: Wikipedia dataset for sl, parsed from 20190301 dump. In addition to prizes for the top teams, there is a special set of awards for using TensorFlow 2.0 APIs. [12] In 2009, the team, led by Geoffrey Hinton, had implemented generalized backpropagation and other improvements which allowed generation of neural networks with substantially higher accuracy, for instance a 25% reduction in errors in speech recognition.[13]. Classes labelled, training/validation/testing set splits created by benchmark scripts. Config description: Wikipedia dataset for kab, parsed from 20200301 dump. Video dataset for action localization and spotting. Config description: Wikipedia dataset for csb, parsed from 20190301 The dataset is provided by Google's Natural Questions, but contains its own unique private test set. Goal is to determine set of rules that governs the network. We leverage the powerful Universal Sentence Encoder (USE) and stitch it with a fast indexing library (Annoy Indexing) to build our system. Each item added to annoy index should be given a unique index. In this post, we demonstrate how to build a tool that can return similar sentences from a corpus for a given input sentence.

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