fasttext

Recently used in a project fasttext[1], this is the open source of Facebook this year a word vector and text Classification tool, there is no academic innovation, but the advantage is simple model, training speed is very fast. I tried it in a recent project and found that it was really handy to use, and that the results could be used on-line. In fact, the model used by Fasttext and Word2vec model is the sam

Programmers who have transformed into AI have followed this number??? Big Data Mining DT data Analysis public number: DATADW First exposure to text forecasts because of participating in the Big Data contest organized by Datafountain and CCF. Comparing some models, the final decision is to try Fasttext. The process of getting started Fasttext can be said to be very painful, because there are few

The algorithm was open source by Facebook in 2016, and the typical application scenario was "supervised text categorization issues". ModelThe optimization objectives of the model are as follows:Among them, $The optimization target is represented as a graph model as follows:The difference from Word2vecThere are many similarities between this model and Word2vec, and there are many different places. Similar places let these two algorithms differ in place to let these twoA similar place: Th

Fasttext, a fast text classifier developed by Facebook, provides a simple and efficient way to categorize and characterize text, but there are two parts to this project. Introduction to the Theory blog: Nlp︱ advanced word vector expression (ii)--fasttext (brief, learning notes) This new Fastrtext has also inherited two functions: training Word vector + text classification model training Source: Https://gith

Glove uses the co-occurrence (co-occurrence) information between the words and the words, assuming that the element Xij is the number of times the word J appears in the Environment (context) of the word I. There are many possible definitions of the "environment" here. For example, in a text sequence, if the word J appears on the left side of the word I or on the right side of the distance of no more than 10 words, we think that the word J appears in the environment of the word I once. The probab

Text categorization problem: given document P (may contain title T), categorize documents into one or more of the N categoriesText classification applications: Common spam identification, sentiment analysisText classification direction: mainly two categories, multi-classification, multi-label classificationText Classification methods: Traditional machine learning Methods (Bayesian, SVM, etc.), deep learning methods (FASTTEXT,TEXTCNN, etc.)The processi

What machine learning programs have been the most watched in 2017 years. Mybridge a list of top 30 for us, with GitHub links attached to all of the following items. We compared nearly 8,800 Kaiyuan machine learning programs and selected the best of the 30. This is a very competitive list of all the outstanding machine learning libraries, datasets, and applications for January 2017-December open source. Mybridge AI Ratings by popularity, participation, and freshness. Let me first give you a visu

data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which are a conventional language model in natural language Processing. The second approach is to use a sequence autoencoder ... "Result: "With pretraining, we is able to train long short term memory recurrent networks up to a few hundred timesteps, t Hereby achieving strong performance in many text classification tasks, such as IMDB, DBpedia and newsgroups. "Bag of Tricks

Source: paperweekly This article a total of 900 characters, recommended to read 6 minutes.This article lists the top ten interesting machine learning open source projects for you recently GitHub.-01- Face recognition #世界上最简单的人脸识别库 This project is known as the simplest face recognition library in the world and can be invoked using Python and the command line. The library is built using the Dlib-top depth learning face recognition technology, with an accuracy rate of 99.38% on the outdoor Face

Selected top 32 machine learning open source project, organized from Mybridge AI: 1. Fasttext: Quick text representation and text classification library (11786 stars on GitHub, contributor Facebook) SOURCE Link: Https://github.com/facebookresearch/MUSE 2. Deep-photo-styletransfer: "Deep photo Style Transfer" The source and data of this paper. (GitHub 9747 stars, papers from Cornell University's Fujun Luan) SOURCE Link: Https://github.com/luanfujun/de

: Build Artificial neural network (recommended number of times 68,745 stars) Links: Http://bit.ly/2CH1WcQ B. Complete guide to deep learning in Python TensorFlow (recommended 17,834, 4.6/5 stars) Links: Http://bit.ly/2EatVy7 Next is a selection of top 30 items from the Mybridge: 1.FastText: Fast text representation and text classification library (11786 stars on GitHub, contributors Facebook SOURCE Link: Https://github.com/facebookresearch/MUSE 2.dee

theme model in the company's recommendations, search and other business has been widely used, the use of methods according to their respective business. The neural network model represented by Word2vec is widely used in recent years, such as clustering of words, discovery of synonyms, extension of quer y, extension of recommendation interest, and so on. Facebook has developed a Word2vec alternative Fasttext, which, based on the traditional word vecto