Uses the API from this great article on Emoji & Deep Learning. Check out the Dango app if you want something like this on your phone.Works best on macOS. Terminals on Linux render emojis in monochrome as they don't support color emojis. On Linux, I would recommend installing Emoji One for full emoji coverage. Doesn't really work on Windows.
https://github.com/sindresorhus/emojTags | cli-app cli emoji emojis emoj emoticon search find matching relevant neural networks |
Implementation | Javascript |
License | MIT |
Platform | NodeJS |
Note you must have node and npm installed. If you don't, go to nodejs.org and follow the install instructions there. Note that the emoji pack to upload can be a path to a yaml file on your machine or a URL, like http://www.emojipacks.com/packs/food.yaml.
slack emoji emojis chat fun lol lols lulz derp slacker slackin communication emoticon emoticonsEmoji Scavenger Hunt is an experimental web based game that makes use of TensorFlow.js to identify objects seen by your webcam or mobile camera in the browser. We show you emojis 🍌 ⏰ ☕️ 📱 and you have to find those objects in the real world before your timer runs out 🏆 👍. Running yarn prep will use yarn to get the right packages and setup the right folders. If you don't have yarn you can install it via homebrew (for Mac). If you’re already running node/npm with nvm (our recommendation) you can install yarn without node using brew install yarn --without-node.
uni queries the Unicode database from the commandline. It supports Unicode 14.0 (September 2021) and has good support for emojis. There are four commands: identify codepoints in a string, search for codepoints, print codepoints by class, block, or range, and emoji to find emojis.
emoji unicode emoji-pickerThe Sindre Sorhus CLI
sindresorhus sindre cli-app business-card nodejs npm-package npx cli unicorns:smirk: simple emoji support for node.js projects
emoji node nodejs emoji-support node-emoji simple emoticons emoticon emojis smiley smileys smilies ideogram ideogramsA gitmoji interactive client for using gitmojis on commit messages. You can use the commit functionality in two ways, directly or via a commit-hook.
gitmoji-cli gitmoji emoji cli carloscuesta commitDeText is a Deep Text understanding framework for NLP related ranking, classification, and language generation tasks. It leverages semantic matching using deep neural networks to understand member intents in search and recommender systems. As a general NLP framework, DeText can be applied to many tasks, including search & recommendation ranking, multi-class classification and query understanding tasks.
nlp deep-neural-networks ranking classification text-embeddings detext-framework neural-networksSuperGlue is a CVPR 2020 research project done at Magic Leap. The SuperGlue network is a Graph Neural Network combined with an Optimal Matching layer that is trained to perform matching on two sets of sparse image features. This repo includes PyTorch code and pretrained weights for running the SuperGlue matching network on top of SuperPoint keypoints and descriptors. Given a pair of images, you can use this repo to extract matching features across the image pair. Full paper PDF: SuperGlue: Learning Feature Matching with Graph Neural Networks.
deep-learning pose-estimation feature-matching graph-neural-networksQdrant ( quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more.
search-engine elasticsearch neural-network matching filter saas nearest-neighbor-search image-search recommender-system vectors approximate-nearest-neighbor-search knn-algorithm hnsw vector-search vector-search-engine embeddings-similarity semantic-searchThingscoop is a command-line utility for analyzing videos semantically - that means searching, filtering, and describing videos based on objects, places, and other things that appear in them.When you first run thingscoop on a video file, it uses a convolutional neural network to create an "index" of what's contained in the every second of the input by repeatedly performing image classification on a frame-by-frame basis. Once an index for a video file has been created, you can search (i.e. get the start and end times of the regions in the video matching the query) and filter (i.e. create a supercut of the matching regions) the input using arbitrary queries. Thingscoop uses a very basic query language that lets you to compose queries that test for the presence or absence of labels with the logical operators ! (not), || (or) and && (and). For example, to search a video the presence of the sky and the absence of the ocean: thingscoop search 'sky && !ocean' <file>.
A simple library to add Emoji support to your Android app. In a PopupWindow Emojis can be chosen. In order to edit and display text with Emojis this library provides public APIs: EmojiEditText, EmojiTextView & EmojiButton. The library has 4 different providers to choose from (iOS, EmojiOne, Google & Twitter).
android emoji ios-emojis emojioneOur Sliding Emoji Keyboard app. Basic keyboard application that allows you to insert emojis. It has a settings activity that allows you to switch between iOS and Android style emojis.
emoji android keyboardGitmoji is an initiative to standardize and explain the use of emojis on GitHub commit messages. Using emojis on commit messages provides an easy way of identifying the purpose or intention of a commit with only looking at the emojis used. As there are a lot of different emojis I found the need of creating a guide that can help to use emojis easier.
emoji gitmoji commitsA menubar app adaptation of Emoji searcher. After installation, find Mojibar in your apps folder or search Mojibar in spotlight. Mojibar will appear in your tray at the top right corner of your screen.
emoji menubar electronThis repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. These topics are organised into three high level themes forming a progression from understanding the use of neural networks for sequential language modelling, to understanding their use as conditional language models for transduction tasks, and finally to approaches employing these techniques in combination with other mechanisms for advanced applications. Throughout the course the practical implementation of such models on CPU and GPU hardware is also discussed.
deep-learning machine-learning natural-language-processing nlp oxfordAs of 2015/07/10, the emoji keyword library has been migrated to its own repository muan/emojilib. There are almost 900 emoji, more keywords let you find emoji more easily. Go to emojis.json for the list of emoji & keywords.
emoji searchConvNetJS is a Javascript implementation of Neural networks, It currently supports Common Neural Network modules, Classification (SVM/Softmax) and Regression (L2) cost functions, A MagicNet class for fully automatic neural network learning (automatic hyperparameter search and cross-validatations), Ability to specify and train Convolutional Networks that process images, An experimental Reinforcement Learning module, based on Deep Q Learning.
artificial-intelligence neural-networks machine-learning deep-learningEcoji encodes data as 1024 emojis, its base1024 with an emoji character set. As a bonus, includes code to decode emojis to original data. The difference between Ecoji and Base64 is that Ecoji is more bytes, but less visible characters. With Ecoji each visible char represents 10 bits, but each character is multi-byte. With base64 each char represents 6 bits and is one byte.
emoji unicode encoding ecoji security base64Tantivy is a full text search engine library written in rust. It is closer to Lucene than to Elastic Search and Solr in the sense it is not an off-the-shelf search engine server, but rather a crate that can be used to build such a search engine.
search-engine searchengine full-text-search lucene library
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