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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
<html>
<head>
<meta name="generator" content="http://txt2tags.org">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<title>UDPipe 1</title>
</head>
<body>
<div class="header" id="header">
<h1>UDPipe 1</h1>
<h2>Version 1.3.2-dev</h2>
</div>
<div class="toc">
<ol>
<li><a href="#introduction">Introduction</a>
</li>
<li><a href="#online">Online Web Application and Web Service</a>
</li>
<li><a href="#release">Release</a>
<ul>
<li><a href="#download">3.1. Download</a>
<ul>
<li><a href="#available_models">3.1.1. Available Models</a>
</li>
</ul>
</li>
<li><a href="#license">3.2. License</a>
</li>
</ul>
</li>
<li><a href="#installation">UDPipe Installation</a>
<ul>
<li><a href="#requirements">4.1. Requirements</a>
</li>
<li><a href="#compilation">4.2. Compilation</a>
<ul>
<li><a href="#compilation_platforms">4.2.1. Platforms</a>
</li>
<li><a href="#compilation_further_details">4.2.2. Further Details</a>
</li>
</ul>
</li>
<li><a href="#other_language_bindings">4.3. Other language bindings</a>
<ul>
<li><a href="#csharp_installation">4.3.1. C#</a>
</li>
<li><a href="#java_installation">4.3.2. Java</a>
</li>
<li><a href="#perl_installation">4.3.3. Perl</a>
</li>
<li><a href="#python_installation">4.3.4. Python</a>
</li>
</ul>
</li>
</ul>
</li>
<li><a href="#models">UDPipe Models</a>
<ul>
<li><a href="#universal_dependencies_25_models">5.1. Universal Dependencies 2.5 Models</a>
<ul>
<li><a href="#universal_dependencies_25_models_download">5.1.1. Download</a>
</li>
<li><a href="#universal_dependencies_25_models_acknowledgements">5.1.2. Acknowledgements</a>
</li>
<li><a href="#universal_dependencies_25_models_description">5.1.3. Model Description</a>
</li>
<li><a href="#universal_dependencies_25_models_performance">5.1.4. Model Performance</a>
</li>
</ul>
</li>
<li><a href="#universal_dependencies_24_models">5.2. Universal Dependencies 2.4 Models</a>
<ul>
<li><a href="#universal_dependencies_24_models_download">5.2.1. Download</a>
</li>
<li><a href="#universal_dependencies_24_models_acknowledgements">5.2.2. Acknowledgements</a>
</li>
<li><a href="#universal_dependencies_24_models_description">5.2.3. Model Description</a>
</li>
<li><a href="#universal_dependencies_24_models_performance">5.2.4. Model Performance</a>
</li>
</ul>
</li>
<li><a href="#universal_dependencies_23_models">5.3. Universal Dependencies 2.3 Models</a>
<ul>
<li><a href="#universal_dependencies_23_models_download">5.3.1. Download</a>
</li>
<li><a href="#universal_dependencies_23_models_acknowledgements">5.3.2. Acknowledgements</a>
</li>
<li><a href="#universal_dependencies_23_models_description">5.3.3. Model Description</a>
</li>
<li><a href="#universal_dependencies_23_models_performance">5.3.4. Model Performance</a>
</li>
</ul>
</li>
<li><a href="#conll18_shared_task_baseline_ud_22_models">5.4. CoNLL18 Shared Task Baseline UD 2.2 Models</a>
<ul>
<li><a href="#conll18_shared_task_baseline_ud_22_models_download">5.4.1. Download</a>
</li>
<li><a href="#conll18_shared_task_baseline_ud_22_models_ackowledgements">5.4.2. Acknowledgements</a>
</li>
</ul>
</li>
<li><a href="#universal_dependencies_20_models">5.5. Universal Dependencies 2.0 Models</a>
<ul>
<li><a href="#universal_dependencies_20_models_download">5.5.1. Download</a>
</li>
<li><a href="#universal_dependencies_20_models_acknowledgements">5.5.2. Acknowledgements</a>
</li>
<li><a href="#universal_dependencies_20_models_description">5.5.3. Model Description</a>
</li>
<li><a href="#universal_dependencies_20_models_performance">5.5.4. Model Performance</a>
</li>
</ul>
</li>
<li><a href="#conll17_shared_task_baseline_ud_20_models">5.6. CoNLL17 Shared Task Baseline UD 2.0 Models</a>
<ul>
<li><a href="#conll17_shared_task_baseline_ud_20_models_download">5.6.1. Download</a>
</li>
<li><a href="#conll17_shared_task_baseline_ud_20_models_ackowledgements">5.6.2. Acknowledgements</a>
</li>
</ul>
</li>
<li><a href="#universal_dependencies_12_models">5.7. Universal Dependencies 1.2 Models</a>
<ul>
<li><a href="#universal_dependencies_12_models_download">5.7.1. Download</a>
</li>
<li><a href="#universal_dependencies_12_models_acknowledgements">5.7.2. Acknowledgements</a>
</li>
<li><a href="#universal_dependencies_12_models_description">5.7.3. Model Description</a>
</li>
<li><a href="#universal_dependencies_12_models_performance">5.7.4. Model Performance</a>
</li>
</ul>
</li>
</ul>
</li>
<li><a href="#users_manual">UDPipe User's Manual</a>
<ul>
<li><a href="#run_udpipe">6.1. Running UDPipe</a>
<ul>
<li><a href="#run_udpipe_immediate">6.1.1. Immediate Mode</a>
</li>
<li><a href="#run_udpipe_model_on_demand">6.1.2. Loading Model On Demand</a>
</li>
<li><a href="#run_udpipe_tokenizer">6.1.3. Tokenizer</a>
</li>
<li><a href="#run_udpipe_input">6.1.4. Input Formats</a>
</li>
<li><a href="#run_udpipe_tagger">6.1.5. Tagger</a>
</li>
<li><a href="#run_udpipe_parser">6.1.6. Dependency Parsing</a>
</li>
<li><a href="#run_udpipe_output">6.1.7. Output Formats</a>
</li>
</ul>
</li>
<li><a href="#udpipe_server">6.2. Running the UDPipe REST Server</a>
</li>
<li><a href="#model_training">6.3. Training UDPipe Models</a>
<ul>
<li><a href="#model_training_reusing_components">6.3.1. Reusing Components from Existing Models</a>
</li>
<li><a href="#model_training_random_search">6.3.2. Random Hyperparameter Search</a>
</li>
<li><a href="#model_training_tokenizer">6.3.3. Tokenizer</a>
</li>
<li><a href="#model_training_tagger">6.3.4. Tagger</a>
</li>
<li><a href="#model_training_parser">6.3.5. Parser</a>
</li>
<li><a href="#udpipe_accuracy">6.3.6. Measuring Model Accuracy</a>
</li>
</ul>
</li>
</ul>
</li>
<li><a href="#api_reference">UDPipe API Reference</a>
<ul>
<li><a href="#versioning">7.1. UDPipe Versioning</a>
</li>
<li><a href="#string_piece">7.2. Struct string_piece</a>
</li>
<li><a href="#token">7.3. Class token</a>
<ul>
<li><a href="#token_get_space_after">7.3.1. token::get_space_after()</a>
</li>
<li><a href="#token_set_space_after">7.3.2. token::set_space_after()</a>
</li>
<li><a href="#token_get_spaces_before">7.3.3. token::get_spaces_before()</a>
</li>
<li><a href="#token_set_spaces_before">7.3.4. token::set_spaces_before()</a>
</li>
<li><a href="#token_get_spaces_after">7.3.5. token::get_spaces_after()</a>
</li>
<li><a href="#token_set_spaces_after">7.3.6. token::set_spaces_after()</a>
</li>
<li><a href="#token_get_spaces_in_token">7.3.7. token::get_spaces_in_token()</a>
</li>
<li><a href="#token_set_spaces_in_token">7.3.8. token::set_spaces_in_token()</a>
</li>
<li><a href="#token_get_token_range">7.3.9. token::get_token_range()</a>
</li>
<li><a href="#token_set_token_range">7.3.10. token::set_token_range()</a>
</li>
</ul>
</li>
<li><a href="#word">7.4. Class word</a>
</li>
<li><a href="#multiword_token">7.5. Class multiword_token</a>
</li>
<li><a href="#empty_node">7.6. Class empty_node</a>
</li>
<li><a href="#sentence">7.7. Class sentence</a>
<ul>
<li><a href="#sentence_empty">7.7.1. sentence::empty()</a>
</li>
<li><a href="#sentence_clear">7.7.2. sentence::clear()</a>
</li>
<li><a href="#sentence_add_word">7.7.3. sentence::add_word()</a>
</li>
<li><a href="#sentence_set_head">7.7.4. sentence:set_head()</a>
</li>
<li><a href="#sentence_unlink_all_words">7.7.5. sentence::unlink_all_words()</a>
</li>
<li><a href="#sentence_get_new_doc">7.7.6. sentence::get_new_doc()</a>
</li>
<li><a href="#sentence_set_new_doc">7.7.7. sentence::set_new_doc()</a>
</li>
<li><a href="#sentence_get_new_par">7.7.8. sentence::get_new_par()</a>
</li>
<li><a href="#sentence_set_new_par">7.7.9. sentence::set_new_par()</a>
</li>
<li><a href="#sentence_get_sent_id">7.7.10. sentence::get_sent_id()</a>
</li>
<li><a href="#sentence_set_sent_id">7.7.11. sentence::set_sent_id()</a>
</li>
<li><a href="#sentence_get_text">7.7.12. sentence::get_text()</a>
</li>
<li><a href="#sentence_set_text">7.7.13. sentence::set_text()</a>
</li>
</ul>
</li>
<li><a href="#input_format">7.8. Class input_format</a>
<ul>
<li><a href="#input_format_read_block">7.8.1. input_format::read_block()</a>
</li>
<li><a href="#input_format_reset_document">7.8.2. input_format::reset_document()</a>
</li>
<li><a href="#input_format_set_text">7.8.3. input_format::set_text()</a>
</li>
<li><a href="#input_format_next_sentence">7.8.4. input_format::next_sentence()</a>
</li>
<li><a href="#input_format_new_input_format">7.8.5. input_format::new_input_format()</a>
</li>
<li><a href="#input_format_new_conllu_input_format">7.8.6. input_format::new_conllu_input_format()</a>
</li>
<li><a href="#input_format_new_generic_tokenizer_input_format">7.8.7. input_format::new_generic_tokenizer_input_format()</a>
</li>
<li><a href="#input_format_new_horizontal_input_format">7.8.8. input_format::new_horizontal_input_format()</a>
</li>
<li><a href="#input_format_new_vertical_input_format">7.8.9. input_format::new_vertical_input_format()</a>
</li>
<li><a href="#input_format_new_presegmented_tokenizer">7.8.10. input_format::new_presegmented_tokenizer()</a>
</li>
</ul>
</li>
<li><a href="#output_format">7.9. Class output_format</a>
<ul>
<li><a href="#output_format_write_sentence">7.9.1. output_format::write_sentence()</a>
</li>
<li><a href="#output_format_finish_document">7.9.2. output_format::finish_document()</a>
</li>
<li><a href="#output_format_new_output_format">7.9.3. output_format::new_output_format()</a>
</li>
<li><a href="#output_format_new_conllu_output_format">7.9.4. output_format::new_conllu_output_format()</a>
</li>
<li><a href="#output_format_new_epe_output_format">7.9.5. output_format::new_epe_output_format()</a>
</li>
<li><a href="#output_format_new_matxin_output_format">7.9.6. output_format::new_matxin_output_format()</a>
</li>
<li><a href="#output_format_new_plaintext_output_format">7.9.7. output_format::new_plaintext_output_format()</a>
</li>
<li><a href="#output_format_new_horizontal_output_format">7.9.8. output_format::new_horizontal_output_format()</a>
</li>
<li><a href="#output_format_new_vertical_output_format">7.9.9. output_format::new_vertical_output_format()</a>
</li>
</ul>
</li>
<li><a href="#model">7.10. Class model</a>
<ul>
<li><a href="#model_load_cstring">7.10.1. model::load(const char*)</a>
</li>
<li><a href="#model_load_istream">7.10.2. model::load(istream&)</a>
</li>
<li><a href="#model_new_tokenizer">7.10.3. model::new_tokenizer()</a>
</li>
<li><a href="#model_tag">7.10.4. model::tag()</a>
</li>
<li><a href="#model_parse">7.10.5. model::parse()</a>
</li>
</ul>
</li>
<li><a href="#pipeline">7.11. Class pipeline</a>
<ul>
<li><a href="#pipeline_set_model">7.11.1. pipeline::set_model()</a>
</li>
<li><a href="#pipeline_set_input">7.11.2. pipeline::set_input()</a>
</li>
<li><a href="#pipeline_set_tagger">7.11.3. pipeline::set_tagger()</a>
</li>
<li><a href="#pipeline_set_parser">7.11.4. pipeline::set_parser()</a>
</li>
<li><a href="#pipeline_set_output">7.11.5. pipeline::set_output()</a>
</li>
<li><a href="#pipeline_set_immediate">7.11.6. pipeline::set_immediate()</a>
</li>
<li><a href="#pipeline_set_document_id">7.11.7. pipeline::set_document_id()</a>
</li>
<li><a href="#pipeline_process">7.11.8. pipeline::process()</a>
</li>
</ul>
</li>
<li><a href="#trainer">7.12. Class trainer</a>
<ul>
<li><a href="#trainer_train">7.12.1. trainer::train()</a>
</li>
</ul>
</li>
<li><a href="#evaluator">7.13. Class evaluator</a>
<ul>
<li><a href="#evaluator_set_model">7.13.1. evaluator::set_model()</a>
</li>
<li><a href="#evaluator_set_tokenizer">7.13.2. evaluator::set_tokenizer()</a>
</li>
<li><a href="#evaluator_set_tagger">7.13.3. evaluator::set_tagger()</a>
</li>
<li><a href="#evaluator_set_parser">7.13.4. evaluator::set_parser()</a>
</li>
<li><a href="#evaluator_evaluate">7.13.5. evaluator::evaluate()</a>
</li>
</ul>
</li>
<li><a href="#version">7.14. Class version</a>
<ul>
<li><a href="#version_current">7.14.1. version::current</a>
</li>
</ul>
</li>
<li><a href="#cpp_bindings_api">7.15. C++ Bindings API</a>
<ul>
<li><a href="#bindings_helper_structures">7.15.1. Helper Structures</a>
</li>
<li><a href="#bindings_main_classes">7.15.2. Main Classes</a>
</li>
</ul>
</li>
<li><a href="#csharp_bindings">7.16. C# Bindings</a>
</li>
<li><a href="#java_bindings">7.17. Java Bindings</a>
</li>
<li><a href="#perl_bindings">7.18. Perl Bindings</a>
</li>
<li><a href="#python_bindings">7.19. Python Bindings</a>
</li>
</ul>
</li>
<li><a href="#contact">Contact</a>
</li>
<li><a href="#udpipe_acknowledgements">Acknowledgements</a>
<ul>
<li><a href="#publications">9.1. Publications</a>
</li>
<li><a href="#bibtex_for_referencing">9.2. Bibtex for Referencing</a>
</li>
<li><a href="#persistent_identifier">9.3. Persistent Identifier</a>
</li>
</ul>
</li>
</ol>
</div>
<div class="body" ID="body">
<a id="introduction" name="introduction"></a>
<h1>1. Introduction</h1>
<p>
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 <a href="http://universaldependencies.org/format.html">CoNLL-U format</a>. Trained models are provided for
nearly all <a href="http://universaldependencies.org">UD treebanks</a>. 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.
<a href="https://CRAN.R-project.org/package=udpipe">Third-party R CRAN package</a> also exists.
</p>
<p>
UDPipe is a free software distributed under the
<a href="http://www.mozilla.org/MPL/2.0/">Mozilla Public License 2.0</a> and the linguistic models
are free for non-commercial use and distributed under the
<a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA</a> license, although for some
models the original data used to create the model may impose additional
licensing conditions. UDPipe is versioned using <a href="http://semver.org/">Semantic Versioning</a>.
</p>
<p>
Copyright 2017 by Institute of Formal and Applied Linguistics, Faculty of
Mathematics and Physics, Charles University, Czech Republic.
</p>
<a id="online" name="online"></a>
<h1>2. Online Web Application and Web Service</h1>
<p>
UDPipe Web Application is available at <a href="http://lindat.mff.cuni.cz/services/udpipe/">http://lindat.mff.cuni.cz/services/udpipe/</a>
using <a href="http://lindat.cz">LINDAT/CLARIN infrastructure</a>.
</p>
<p>
UDPipe REST Web Service is also available, with the API documentation available at
<a href="http://lindat.mff.cuni.cz/services/udpipe/api-reference.php">http://lindat.mff.cuni.cz/services/udpipe/api-reference.php</a>.
</p>
<a id="release" name="release"></a>
<h1>3. Release</h1>
<a id="download" name="download"></a>
<h2>3.1. Download</h2>
<p>
UDPipe releases are available on <a href="http://github.com/ufal/udpipe">GitHub</a>, both as
source code and as a pre-compiled binary package. The binary package contains Linux,
Windows and OS X binaries, Java bindings binary, C# bindings binary, and source
code of UDPipe and all language bindings). While the binary
packages do not contain compiled Python or Perl bindings, packages for those
languages are available in standard package repositories,
i.e. on <a href="https://pypi.python.org/pypi/ufal.udpipe/">PyPI</a>
and <a href="https://metacpan.org/pod/Ufal::UDPipe">CPAN</a>.
</p>
<ul>
<li><a href="http://github.com/ufal/udpipe/releases/latest">Latest release</a>
</li>
<li><a href="http://github.com/ufal/udpipe/releases">All releases</a>, <a href="https://github.com/ufal/udpipe/blob/master/CHANGES">Changelog</a>
</li>
</ul>
<p>
You might also be interested in a contributed package
<a href="https://github.com/TakeLab/spacy-udpipe">spacy-udpipe</a> which wraps UDPipe
with spaCy API.
</p>
<a id="available_models" name="available_models"></a>
<h3>3.1.1. Available Models</h3>
<p>
To use UDPipe, a model is needed. The models are available
from <a href="http://www.lindat.cz">LINDAT/CLARIN</a> infrastructure and described further
in the
<a href="#models">UDPipe Models</a>.
Currently, the following models are available:
</p>
<ul>
<li>Universal Dependencies 2.5 Models: <a href="http://hdl.handle.net/11234/1-3131">udpipe-ud2.5-191206</a> (<a href="http://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_25_models">documentation</a>)
</li>
<li>Universal Dependencies 2.4 Models: <a href="http://hdl.handle.net/11234/1-2998">udpipe-ud2.4-190531</a> (<a href="http://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_24_models">documentation</a>)
</li>
<li>Universal Dependencies 2.3 Models: <a href="http://hdl.handle.net/11234/1-2898">udpipe-ud2.3-181115</a> (<a href="http://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_23_models">documentation</a>)
</li>
<li>CoNLL18 Shared Task Baseline UD 2.2 Models: <a href="http://hdl.handle.net/11234/1-2859">udpipe-ud2.2-conll18-180430</a> (<a href="http://ufal.mff.cuni.cz/udpipe/1/models#conll18_shared_task_baseline_ud_22_models">documentation</a>)
</li>
<li>Universal Dependencies 2.0 Models: <a href="http://hdl.handle.net/11234/1-2364">udpipe-ud2.0-170801</a> (<a href="http://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_20_models">documentation</a>)
</li>
<li>CoNLL17 Shared Task Baseline UD 2.0 Models: <a href="http://hdl.handle.net/11234/1-1990">udpipe-ud2.0-conll17-170315</a> (<a href="http://ufal.mff.cuni.cz/udpipe/1/models#conll17_shared_task_baseline_ud_20_models">documentation</a>)
</li>
<li>Universal Dependencies 1.2 Models: <a href="http://hdl.handle.net/11234/1-1659">udpipe-ud1.2-160523</a> (<a href="http://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_12_models">documentation</a>)
</li>
</ul>
<a id="license" name="license"></a>
<h2>3.2. License</h2>
<p>
UDPipe is an open-source project and is freely available for non-commercial
purposes. The library is distributed under
<a href="http://www.mozilla.org/MPL/2.0/">Mozilla Public License 2.0</a> and the associated models and data
under <a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA</a>, although
for some models the original data used to create the model may impose
additional licensing conditions.
</p>
<p>
If you use this tool for scientific work, please give credit to us by
referencing <a href="#bibtex_for_referencing">Straka et al. 2016</a> and the
<a href="http://ufal.mff.cuni.cz/udpipe">UDPipe website</a>.
</p>
<a id="installation" name="installation"></a>
<h1>4. UDPipe Installation</h1>
<p>
UDPipe releases are available on <a href="http://github.com/ufal/udpipe">GitHub</a>, either as
a pre-compiled binary package, or source code only. The binary package contains Linux,
Windows and OS X binaries, Java bindings binary, C# bindings binary, and source
code of UDPipe and all language bindings. While the binary
packages do not contain compiled Python or Perl bindings, packages for those
languages are available in standard package repositories, i.e. on PyPI and CPAN.
</p>
<p>
To use UDPipe, a model is needed.
<a href="http://ufal.mff.cuni.cz/udpipe/1#available_models">Here is a list of available models</a>.
</p>
<p>
If you want to compile UDPipe manually, sources are available on on
<a href="http://github.com/ufal/udpipe">GitHub</a>, both in the
<a href="http://github.com/ufal/udpipe/releases">pre-compiled binary package releases</a>
and in the repository itself.
</p>
<a id="requirements" name="requirements"></a>
<h2>4.1. Requirements</h2>
<ul>
<li><code>g++ 4.7</code> or newer, <code>clang 3.2</code> or newer, Visual C++ 2015 or newer
</li>
<li><code>make</code>
</li>
<li><code>SWIG 3.0.8</code> or newer for language bindings other than <code>C++</code>
</li>
</ul>
<a id="compilation" name="compilation"></a>
<h2>4.2. Compilation</h2>
<p>
To compile UDPipe, run <code>make</code> in the <code>src</code> directory.
</p>
<p style="margin-bottom:0">
Make targets and options:
</p>
<ul style="margin-top:0">
<li><code>exe</code>: compile the binaries (default)
</li>
<li><code>server</code>: compile the REST server
</li>
<li><code>lib</code>: compile the static library
</li>
<li><code>BITS=32</code> or <code>BITS=64</code>: compile for specified 32-bit or 64-bit architecture instead of the default one
</li>
<li><code>MODE=release</code>: create release build which statically links the C++ runtime and uses LTO
</li>
<li><code>MODE=debug</code>: create debug build
</li>
<li><code>MODE=profile</code>: create profile build
</li>
</ul>
<a id="compilation_platforms" name="compilation_platforms"></a>
<h3>4.2.1. Platforms</h3>
<p style="margin-bottom:0">
Platform can be selected using one of the following options:
</p>
<ul style="margin-top:0">
<li><code>PLATFORM=linux</code>, <code>PLATFORM=linux-gcc</code>: gcc compiler on Linux operating system, default on Linux
</li>
<li><code>PLATFORM=linux-clang</code>: clang compiler on Linux, must be selected manually
</li>
<li><code>PLATFORM=macos</code>, <code>PLATFORM=macos-clang</code>: clang compiler on OS X, default on OS X; <code>BITS=32+64</code> enables multiarch build
</li>
<li><code>PLATFORM=win</code>, <code>PLATFORM=win-gcc</code>: gcc compiler on Windows (TDM-GCC is well tested), default on Windows
</li>
<li><code>PLATFORM=win-vs</code>: Visual C++ 2015 compiler on Windows, must be selected manually; note that the
<code>cl.exe</code> compiler must be already present in <code>PATH</code> and corresponding <code>BITS=32</code> or <code>BITS=64</code>
must be specified
</li>
</ul>
<p>
Either POSIX shell or Windows CMD can be used as shell, it is detected automatically.
</p>
<a id="compilation_further_details" name="compilation_further_details"></a>
<h3>4.2.2. Further Details</h3>
<p>
UDPipe uses <a href="http://github.com/ufal/cpp_builtem">C++ BuilTem system</a>,
please refer to its manual if interested in all supported options.
</p>
<a id="other_language_bindings" name="other_language_bindings"></a>
<h2>4.3. Other language bindings</h2>
<a id="csharp_installation" name="csharp_installation"></a>
<h3>4.3.1. C#</h3>
<p>
Binary C# bindings are available in UDPipe binary packages.
</p>
<p>
To compile C# bindings manually, run <code>make</code> in the <code>bindings/csharp</code>
directory, optionally with the options described in UDPipe Installation.
</p>
<a id="java_installation" name="java_installation"></a>
<h3>4.3.2. Java</h3>
<p>
Binary Java bindings are available in UDPipe binary packages.
</p>
<p>
To compile Java bindings manually, run <code>make</code> in the <code>bindings/java</code>
directory, optionally with the options described in UDPipe Installation.
Java 6 and newer is supported.
</p>
<p style="margin-bottom:0">
The Java installation specified in the environment variable <code>JAVA_HOME</code> is
used. If the environment variable does not exist, the <code>JAVA_HOME</code> can be
specified using
</p>
<pre style="margin-top:0">
make JAVA_HOME=path_to_Java_installation
</pre>
<a id="perl_installation" name="perl_installation"></a>
<h3>4.3.3. Perl</h3>
<p>
The Perl bindings are available as <code>Ufal-UDPipe</code> package on CPAN.
</p>
<p>
To compile Perl bindings manually, run <code>make</code> in the <code>bindings/perl</code>
directory, optionally with the options described in UDPipe Installation.
Perl 5.10 and later is supported.
</p>
<p style="margin-bottom:0">
Path to the include headers of the required Perl version must be specified
in the <code>PERL_INCLUDE</code> variable using
</p>
<pre style="margin-top:0">
make PERL_INCLUDE=path_to_Perl_includes
</pre>
<a id="python_installation" name="python_installation"></a>
<h3>4.3.4. Python</h3>
<p>
The Python bindings are available as <code>ufal.udpipe</code> package on PyPI.
</p>
<p>
To compile Python bindings manually, run <code>make</code> in the <code>bindings/python</code>
directory, optionally with options described in UDPipe Installation. Both
Python 2.6+ and Python 3+ are supported.
</p>
<p style="margin-bottom:0">
Path to the include headers of the required Python version must be specified
in the <code>PYTHON_INCLUDE</code> variable using
</p>
<pre style="margin-top:0">
make PYTHON_INCLUDE=path_to_Python_includes
</pre>
<p>
You might also be interested in a contributed package
<a href="https://github.com/TakeLab/spacy-udpipe">spacy-udpipe</a> which wraps UDPipe
with spaCy API.
</p>
<a id="models" name="models"></a>
<h1>5. UDPipe Models</h1>
<p>
Like any supervised machine-learning tool, UDPipe needs a trained linguistic model.
This section describes the available models.
</p>
<a id="universal_dependencies_25_models" name="universal_dependencies_25_models"></a>
<h2>5.1. Universal Dependencies 2.5 Models</h2>
<p>
Universal Dependencies 2.5 Models are distributed under the
<a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA</a> licence.
The models are based solely on
<a href="http://hdl.handle.net/11234/1-3105">Universal Dependencies 2.5</a> treebanks.
The models work in UDPipe version 1.2 and later.
</p>
<p>
Universal Dependencies 2.5 Models are versioned according to the date released
in the format <code>YYMMDD</code>, where <code>YY</code>, <code>MM</code> and <code>DD</code> are two-digit
representation of year, month and day, respectively. The latest version is 191206.
</p>
<a id="universal_dependencies_25_models_download" name="universal_dependencies_25_models_download"></a>
<h3>5.1.1. Download</h3>
<p>
The latest version 190531 of the Universal Dependencies 2.5 models can be downloaded
from <a href="http://hdl.handle.net/11234/1-3131">LINDAT/CLARIN repository</a>.
</p>
<a id="universal_dependencies_25_models_acknowledgements" name="universal_dependencies_25_models_acknowledgements"></a>
<h3>5.1.2. Acknowledgements</h3>
<p>
This work has been partially supported and has been using language resources
and tools developed, stored and distributed by the LINDAT/CLARIN project of the
Ministry of Education, Youth and Sports of the Czech Republic (project <i>LM2015071</i>).
</p>
<p>
The models were trained on <a href="http://hdl.handle.net/11234/1-3105">Universal Dependencies 2.5</a> treebanks.
</p>
<p>
For the UD treebanks which do not contain original plain text version,
raw text is used to train the tokenizer instead. The plain texts
were taken from the <a href="http://hdl.handle.net/11858/00-097C-0000-0022-6133-9">W2C – Web to Corpus</a>.
</p>
<a id="universal_dependencies_25_models_publications" name="universal_dependencies_25_models_publications"></a>
<h4>5.1.2.1. Publications</h4>
<ul>
<li>(Straka et al. 2017) Milan Straka and Jana Straková. <i><a href="http://ufal.mff.cuni.cz/~straka/papers/2017-conll_udpipe.pdf">Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe</a></i>. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, Vancouver, Canada, August 2017.
</li>
<li>(Straka et al. 2016) Straka Milan, Hajič Jan, Straková Jana. <i><a href="http://ufal.mff.cuni.cz/~straka/papers/2016-lrec_udpipe.pdf">UDPipe: Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization, Morphological Analysis, POS Tagging and Parsing</a></i>. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Portorož, Slovenia, May 2016.
</li>
</ul>
<a id="universal_dependencies_25_models_description" name="universal_dependencies_25_models_description"></a>
<h3>5.1.3. Model Description</h3>
<p>
The Universal Dependencies 2.5 models contain 94 models of 61 languages, each consisting of
a tokenizer, tagger, lemmatizer and dependency parser, all trained using
the UD data. We used the original train-dev-test split, but for treebanks with
only train and no dev data we used last 10% of the train data as dev data.
We produce models only for treebanks with at least 1000 training words.
</p>
<p>
The tokenizer is trained using the <code>SpaceAfter=No</code> features. If the features
are not present in the data, they can be filled in using raw text in the
language in question.
</p>
<p>
The tagger, lemmatizer and parser are trained using gold UD data.
</p>
<p>
Details about model architecture and training process can be found in the
(Straka et al. 2017) paper.
</p>
<a id="universal_dependencies_25_reprodusible_training" name="universal_dependencies_25_reprodusible_training"></a>
<h4>5.1.3.1. Reproducible Training</h4>
<p>
In case you want to train the same models, scripts for downloading and
resplitting UD 2.5 data, precomputed word embedding, raw texts for tokenizers,
all hyperparameter values and training scripts are available in the
second archive on the <a href="http://hdl.handle.net/11234/1-3131">model download page</a>.
</p>
<a id="universal_dependencies_25_models_performance" name="universal_dependencies_25_models_performance"></a>
<h3>5.1.4. Model Performance</h3>
<p>
We present the tagger, lemmatizer and parser performance, measured
on the testing portion of the data, evaluated in three different settings:
using raw text only, using gold tokenization only, and using gold tokenization
plus gold morphology (UPOS, XPOS, FEATS and Lemma).
</p>
<table border="1">
<tr>
<th>Treebank</th>
<th>Mode</th>
<th>Words</th>
<th>Sents</th>
<th>UPOS</th>
<th>XPOS</th>
<th>UFeats</th>
<th>AllTags</th>
<th>Lemma</th>
<th>UAS</th>
<th>LAS</th>
<th>MLAS</th>
<th>BLEX</th>
</tr>
<tr>
<td>Afrikaans-AfriBooms</td>
<td>Raw text</td>
<td align="right">99.6%</td>
<td align="right">98.2%</td>
<td align="right">95.0%</td>
<td align="right">90.6%</td>
<td align="center">94.6%</td>
<td align="center">90.6%</td>
<td align="right">96.5%</td>
<td align="right">81.6%</td>
<td align="right">77.6%</td>
<td align="right">64.4%</td>
<td align="right">66.5%</td>
</tr>
<tr>
<td>Afrikaans-AfriBooms</td>
<td>Gold tok</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="right">95.3%</td>
<td align="right">90.8%</td>
<td align="center">94.9%</td>
<td align="center">90.8%</td>
<td align="right">96.7%</td>
<td align="right">82.5%</td>
<td align="right">78.4%</td>
<td align="right">65.1%</td>
<td align="right">67.2%</td>
</tr>
<tr>
<td>Afrikaans-AfriBooms</td>
<td>Gold tok+mor</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="right">87.6%</td>
<td align="right">85.0%</td>
<td align="right">77.0%</td>
<td align="right">79.6%</td>
</tr>
<tr>
<td>Ancient Greek-Perseus</td>
<td>Raw text</td>
<td align="right">100.0%</td>
<td align="right">98.8%</td>
<td align="right">82.2%</td>
<td align="right">72.2%</td>
<td align="center">85.7%</td>
<td align="center">72.2%</td>
<td align="right">82.7%</td>
<td align="right">64.0%</td>
<td align="right">57.0%</td>
<td align="right">30.2%</td>
<td align="right">38.2%</td>
</tr>
<tr>
<td>Ancient Greek-Perseus</td>
<td>Gold tok</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="right">82.2%</td>
<td align="right">72.2%</td>
<td align="center">85.7%</td>
<td align="center">72.2%</td>
<td align="right">82.7%</td>
<td align="right">64.1%</td>
<td align="right">57.2%</td>
<td align="right">30.3%</td>
<td align="right">38.4%</td>
</tr>
<tr>
<td>Ancient Greek-Perseus</td>
<td>Gold tok+mor</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="right">68.7%</td>
<td align="right">63.9%</td>
<td align="right">53.1%</td>
<td align="right">57.2%</td>
</tr>
<tr>
<td>Ancient Greek-PROIEL</td>
<td>Raw text</td>
<td align="right">100.0%</td>
<td align="right">48.0%</td>
<td align="right">96.0%</td>
<td align="right">96.2%</td>
<td align="center">88.6%</td>
<td align="center">87.2%</td>
<td align="right">93.2%</td>
<td align="right">72.2%</td>
<td align="right">67.6%</td>
<td align="right">49.9%</td>
<td align="right">56.0%</td>
</tr>
<tr>
<td>Ancient Greek-PROIEL</td>
<td>Gold tok</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="right">96.1%</td>
<td align="right">96.3%</td>
<td align="center">88.7%</td>
<td align="center">87.4%</td>
<td align="right">93.2%</td>
<td align="right">77.0%</td>
<td align="right">72.1%</td>
<td align="right">54.4%</td>
<td align="right">60.3%</td>
</tr>
<tr>
<td>Ancient Greek-PROIEL</td>
<td>Gold tok+mor</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="right">80.2%</td>
<td align="right">76.4%</td>
<td align="right">66.7%</td>
<td align="right">70.3%</td>
</tr>
<tr>
<td>Arabic-PADT</td>
<td>Raw text</td>
<td align="right">94.6%</td>
<td align="right">82.1%</td>
<td align="right">90.4%</td>
<td align="right">84.0%</td>
<td align="center">84.2%</td>
<td align="center">83.8%</td>
<td align="right">88.5%</td>
<td align="right">72.7%</td>
<td align="right">68.1%</td>
<td align="right">56.2%</td>
<td align="right">59.2%</td>
</tr>
<tr>
<td>Arabic-PADT</td>
<td>Gold tok</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="right">95.6%</td>
<td align="right">89.0%</td>
<td align="center">89.2%</td>
<td align="center">88.8%</td>
<td align="right">92.9%</td>
<td align="right">82.0%</td>
<td align="right">76.8%</td>
<td align="right">63.6%</td>
<td align="right">66.3%</td>
</tr>
<tr>
<td>Arabic-PADT</td>
<td>Gold tok+mor</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="right">83.9%</td>
<td align="right">80.7%</td>
<td align="right">75.7%</td>
<td align="right">76.8%</td>
</tr>
<tr>
<td>Armenian-ArmTDP</td>
<td>Raw text</td>
<td align="right">99.3%</td>
<td align="right">97.8%</td>
<td align="right">92.0%</td>
<td align="center">-</td>
<td align="center">84.7%</td>
<td align="center">83.4%</td>
<td align="right">91.8%</td>
<td align="right">75.6%</td>
<td align="right">68.5%</td>
<td align="right">51.2%</td>
<td align="right">57.4%</td>
</tr>
<tr>
<td>Armenian-ArmTDP</td>
<td>Gold tok</td>
<td align="center">-</td>
<td align="center">-</td>
<td align="right">92.5%</td>
<td align="center">-</td>
<td align="center">85.2%</td>
<td align="center">83.8%</td>
<td align="right">92.4%</td>
<td align="right">76.9%</td>
<td align="right">69.7%</td>
<td align="right">51.5%</td>
<td align="right">57.9%</td>
</tr>
<tr>
<td>Armenian-ArmTDP</td>
<td>Gold tok+mor</td>
<td align="center">-</td>