# UDPipe 1 [![Compile Status](https://github.com/ufal/udpipe/actions/workflows/compile.yml/badge.svg)](https://github.com/ufal/udpipe/actions/workflows/compile.yml) UDPipe is a trainable pipeline for tokenization, tagging, lemmatization and dependency parsing of CoNLL-U files. UDPipe is language-agnostic and can be trained given annotated data in [CoNLL-U format](http://universaldependencies.org/format.html). Trained models are provided for nearly all [UD treebanks](http://universaldependencies.org). UDPipe is available as a binary for Linux/Windows/OS X, as a library for C++, Python, Perl, Java, C#, and as a web service. [Third-party R CRAN package](https://CRAN.R-project.org/package=udpipe) also exists. UDPipe is a free software distributed under the [Mozilla Public License 2.0](http://www.mozilla.org/MPL/2.0/) and the linguistic models are free for non-commercial use and distributed under the [CC BY-NC-SA](http://creativecommons.org/licenses/by-nc-sa/4.0/) license, although for some models the original data used to create the model may impose additional licensing conditions. UDPipe is versioned using [Semantic Versioning](http://semver.org/). Copyright 2017 by Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Czech Republic. UDPipe website http://ufal.mff.cuni.cz/udpipe contains download links of both the released packages and trained models, hosts documentation and offers online web service. UDPipe development repository http://github.com/ufal/udpipe is hosted on GitHub. *Third-party contribution:* Instructions how to build UDPipe REST server as Docker image is here: http://github.com/samisalkosuo/udpipe-rest-server-docker. Instructions how to train UDPipe language models using a Docker image is also there.