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FACTORIE \n\n[![Build Status](https://travis-ci.org/factorie/factorie.svg?branch=master)](https://travis-ci.org/factorie/factorie)\n\nThis directory contains the source of FACTORIE, a toolkit for probabilistic modeling based on imperatively-defined factor graphs. More information, see [the FACTORIE webpage](http://factorie.cs.umass.edu).\n\n## Installation\n\nInstallation relies on Maven, version 3.  If you don't already have maven, install it from http://maven.apache.org/download.html.\nAlternatively, you can use [sbt](http://scala-sbt.org) as outlined below (a script for running sbt comes bundled with Factorie).\n\nTo compile type\n\n```\n$ mvn compile\n```\n\nTo accomplish the same with sbt, type\n\n```\n$ ./sbt compile\n```\n\nYou might need additional memory. If so, for sbt type\n\n```\nexport SBT_OPTS=\"$SBT_OPTS -Xmx1g\"\n```\n\nand for Maven type:\n\n```\nexport MAVEN_OPTS=\"$MAVEN_OPTS -Xmx1g -XX:MaxPermSize=128m\"\n```\n\nTo create a self-contained .jar, that contains FACTORIE plus all its dependencies, including the Scala runtime, type \n\n```\n$ mvn -Dmaven.test.skip=true package -Pjar-with-dependencies\n```\n\nTo accomplish the same with sbt, type\n\n```\n$ ./sbt assembly\n```\n\nTo create a similar self-contained .jar that also contains all resources needed for NLP (including our lexicons and pre-trained model parameters), type  \n\n```\n$ mvn -Dmaven.test.skip=true package -Pnlp-jar-with-dependencies\n```\n\nTo accomplish the same with sbt, type\n\n```\n$ ./sbt -J-Xmx2G with-nlp-resources:assembly\n\n```\n## Try out a simple example\n\nTo get an idea what a simple FACTORIE program might look like, open one of the class files in the tutorial package\n```\n$ ls src/main/scala/cc/factorie/tutorial\n```\nTo run one of these examples using maven type\n```\n$ mvn scala:run -DmainClass=cc.factorie.tutorial.Grid\n```\n\n\n## Try out implemented NLP models\n\nThen you can run some FACTORIE tools from the command-line. For example, you can run many natural language processing tools.\n\n```\n$ bin/fac nlp --wsj-forward-pos --conll-chain-ner\n```\n\nwill launch an NLP server that will perform part-of-speech tagging and named entity recognition in its input.  The server listens for text on a socket, and spawns a parallel document processor on each request.  To feed it input, type in a separate shell\n\n```\n$ echo \"I told Mr. Smith to take a job at IBM in Raleigh.\" | nc localhost 3228\n```\n\nYou can also run a latent Dirichlet allocation (LDA) topic model. Assume that \"mytextdir\" is a directory name containing many plain text documents each in its own file.  Then typing \n\n```\n$ bin/fac lda --read-dirs mytextdir --num-topics 20 --num-iterations 100\n```\n\nwill run 100 iterations of a sparse collapsed Gibbs sampling on all the documents, and print out the results every 10 iterations. FACTORIE's LDA implementation is faster than MALLET's.\n\nYou can also train a document classifier. Assume that \"sportsdir\" and \"politicsdir\" are each directories that  contain plan text files in the categories sports and politics. Typing\n\n```\n$ bin/fac classify --read-text-dirs sportsdir,politicsdir --write-classifier mymodel.factorie\n```\n\nwill train a log-linear by maximum likelihood (MaxEnt) and save it in the file \"mymodel.factorie\".\n\nThe above are simply a few simple command-line options.  Internally the FACTORIE library contains extensive and general facilities for factor graphs: data representation, model structure, inference, learning.\n","funding_links":[],"categories":["Scala","Science and Data Analysis","[](https://github.com/josephmisiti/awesome-machine-learning/blob/master/README.md#scala)Scala"],"sub_categories":["General-Purpose Machine Learning"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffactorie%2Ffactorie","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffactorie%2Ffactorie","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffactorie%2Ffactorie/lists"}