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MANUAL
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MANUAL
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UDPipe 1
Version 1.3.2-dev
Introduction
============
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.
Online Web Application and Web Service
======================================
UDPipe Web Application is available at
http://lindat.mff.cuni.cz/services/udpipe/ using LINDAT/CLARIN infrastructure
(http://lindat.cz).
UDPipe REST Web Service is also available, with the API documentation available
at http://lindat.mff.cuni.cz/services/udpipe/api-reference.php.
Release
=======
Download
--------
UDPipe releases are available on GitHub (http://github.com/ufal/udpipe), 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 PyPI
(https://pypi.python.org/pypi/ufal.udpipe/) and CPAN
(https://metacpan.org/pod/Ufal::UDPipe).
- Latest release (http://github.com/ufal/udpipe/releases/latest)
- All releases (http://github.com/ufal/udpipe/releases), Changelog
(https://github.com/ufal/udpipe/blob/master/CHANGES)
You might also be interested in a contributed package spacy-udpipe
(https://github.com/TakeLab/spacy-udpipe) which wraps UDPipe with spaCy API.
Available Models
----------------
To use UDPipe, a model is needed. The models are available from LINDAT/CLARIN
(http://www.lindat.cz) infrastructure and described further in the UDPipe
Models. Currently, the following models are available:
- Universal Dependencies 2.5 Models: udpipe-ud2.5-191206
(http://hdl.handle.net/11234/1-3131) (documentation
(http://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_25_models))
- Universal Dependencies 2.4 Models: udpipe-ud2.4-190531
(http://hdl.handle.net/11234/1-2998) (documentation
(http://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_24_models))
- Universal Dependencies 2.3 Models: udpipe-ud2.3-181115
(http://hdl.handle.net/11234/1-2898) (documentation
(http://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_23_models))
- CoNLL18 Shared Task Baseline UD 2.2 Models: udpipe-ud2.2-conll18-180430
(http://hdl.handle.net/11234/1-2859) (documentation
(http://ufal.mff.cuni.cz/udpipe/1/models#conll18_shared_task_baseline_ud_22_models))
- Universal Dependencies 2.0 Models: udpipe-ud2.0-170801
(http://hdl.handle.net/11234/1-2364) (documentation
(http://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_20_models))
- CoNLL17 Shared Task Baseline UD 2.0 Models: udpipe-ud2.0-conll17-170315
(http://hdl.handle.net/11234/1-1990) (documentation
(http://ufal.mff.cuni.cz/udpipe/1/models#conll17_shared_task_baseline_ud_20_models))
- Universal Dependencies 1.2 Models: udpipe-ud1.2-160523
(http://hdl.handle.net/11234/1-1659) (documentation
(http://ufal.mff.cuni.cz/udpipe/1/models#universal_dependencies_12_models))
License
-------
UDPipe is an open-source project and is freely available for non-commercial
purposes. The library is distributed under Mozilla Public License 2.0
(http://www.mozilla.org/MPL/2.0/) and the associated models and data under CC
BY-NC-SA (http://creativecommons.org/licenses/by-nc-sa/4.0/), although for some
models the original data used to create the model may impose additional
licensing conditions.
If you use this tool for scientific work, please give credit to us by
referencing Straka et al. 2016 and the UDPipe website
(http://ufal.mff.cuni.cz/udpipe).
UDPipe Installation
===================
UDPipe releases are available on GitHub (http://github.com/ufal/udpipe), 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.
To use UDPipe, a model is needed. Here is a list of available models
(http://ufal.mff.cuni.cz/udpipe/1#available_models).
If you want to compile UDPipe manually, sources are available on on GitHub
(http://github.com/ufal/udpipe), both in the pre-compiled binary package
releases (http://github.com/ufal/udpipe/releases) and in the repository itself.
Requirements
------------
- g++ 4.7 or newer, clang 3.2 or newer, Visual C++ 2015 or newer
- make
- SWIG 3.0.8 or newer for language bindings other than C++
Compilation
-----------
To compile UDPipe, run make in the src directory.
Make targets and options:
- exe: compile the binaries (default)
- server: compile the REST server
- lib: compile the static library
- BITS=32 or BITS=64: compile for specified 32-bit or 64-bit architecture
instead of the default one
- MODE=release: create release build which statically links the C++ runtime and
uses LTO
- MODE=debug: create debug build
- MODE=profile: create profile build
Platforms
---------
Platform can be selected using one of the following options:
- PLATFORM=linux, PLATFORM=linux-gcc: gcc compiler on Linux operating system,
default on Linux
- PLATFORM=linux-clang: clang compiler on Linux, must be selected manually
- PLATFORM=macos, PLATFORM=macos-clang: clang compiler on OS X, default on OS
X; BITS=32+64 enables multiarch build
- PLATFORM=win, PLATFORM=win-gcc: gcc compiler on Windows (TDM-GCC is well
tested), default on Windows
- PLATFORM=win-vs: Visual C++ 2015 compiler on Windows, must be selected
manually; note that the cl.exe compiler must be already present in PATH and
corresponding BITS=32 or BITS=64 must be specified
Either POSIX shell or Windows CMD can be used as shell, it is detected
automatically.
Further Details
---------------
UDPipe uses C++ BuilTem system (http://github.com/ufal/cpp_builtem), please
refer to its manual if interested in all supported options.
Other language bindings
-----------------------
C#
--
Binary C# bindings are available in UDPipe binary packages.
To compile C# bindings manually, run make in the bindings/csharp directory,
optionally with the options described in UDPipe Installation.
Java
----
Binary Java bindings are available in UDPipe binary packages.
To compile Java bindings manually, run make in the bindings/java directory,
optionally with the options described in UDPipe Installation. Java 6 and newer
is supported.
The Java installation specified in the environment variable JAVA_HOME is used.
If the environment variable does not exist, the JAVA_HOME can be specified using
make JAVA_HOME=path_to_Java_installation
Perl
----
The Perl bindings are available as Ufal-UDPipe package on CPAN.
To compile Perl bindings manually, run make in the bindings/perl directory,
optionally with the options described in UDPipe Installation. Perl 5.10 and
later is supported.
Path to the include headers of the required Perl version must be specified in
the PERL_INCLUDE variable using
make PERL_INCLUDE=path_to_Perl_includes
Python
------
The Python bindings are available as ufal.udpipe package on PyPI.
To compile Python bindings manually, run make in the bindings/python directory,
optionally with options described in UDPipe Installation. Both Python 2.6+ and
Python 3+ are supported.
Path to the include headers of the required Python version must be specified in
the PYTHON_INCLUDE variable using
make PYTHON_INCLUDE=path_to_Python_includes
You might also be interested in a contributed package spacy-udpipe
(https://github.com/TakeLab/spacy-udpipe) which wraps UDPipe with spaCy API.
UDPipe Models
=============
Like any supervised machine-learning tool, UDPipe needs a trained linguistic
model. This section describes the available models.
Universal Dependencies 2.5 Models
---------------------------------
Universal Dependencies 2.5 Models are distributed under the CC BY-NC-SA
(http://creativecommons.org/licenses/by-nc-sa/4.0/) licence. The models are
based solely on Universal Dependencies 2.5 (http://hdl.handle.net/11234/1-3105)
treebanks. The models work in UDPipe version 1.2 and later.
Universal Dependencies 2.5 Models are versioned according to the date released
in the format YYMMDD, where YY, MM and DD are two-digit representation of year,
month and day, respectively. The latest version is 191206.
Download
--------
The latest version 190531 of the Universal Dependencies 2.5 models can be
downloaded from LINDAT/CLARIN repository (http://hdl.handle.net/11234/1-3131).
Acknowledgements
----------------
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
LM2015071).
The models were trained on Universal Dependencies 2.5
(http://hdl.handle.net/11234/1-3105) treebanks.
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 W2C
- Web to Corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9).
Publications
------------
- (Straka et al. 2017) Milan Straka and Jana Straková. Tokenizing, POS
Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe
(http://ufal.mff.cuni.cz/~straka/papers/2017-conll_udpipe.pdf). In Proceedings
of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal
Dependencies, Vancouver, Canada, August 2017.
- (Straka et al. 2016) Straka Milan, Hajič Jan, Straková Jana. UDPipe:
Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization,
Morphological Analysis, POS Tagging and Parsing
(http://ufal.mff.cuni.cz/~straka/papers/2016-lrec_udpipe.pdf). In Proceedings
of the Tenth International Conference on Language Resources and Evaluation
(LREC 2016), Portorož, Slovenia, May 2016.
Model Description
-----------------
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.
The tokenizer is trained using the SpaceAfter=No features. If the features are
not present in the data, they can be filled in using raw text in the language in
question.
The tagger, lemmatizer and parser are trained using gold UD data.
Details about model architecture and training process can be found in the
(Straka et al. 2017) paper.
Reproducible Training
---------------------
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 model download page (http://hdl.handle.net/11234/1-3131).
Model Performance
-----------------
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).
|| Treebank | Mode | Words | Sents | UPOS | XPOS | UFeats | AllTags | Lemma | UAS | LAS | MLAS | BLEX |
| Afrikaans-AfriBooms | Raw text | 99.6% | 98.2% | 95.0% | 90.6% | 94.6% | 90.6% | 96.5% | 81.6% | 77.6% | 64.4% | 66.5% |
| Afrikaans-AfriBooms | Gold tok | - | - | 95.3% | 90.8% | 94.9% | 90.8% | 96.7% | 82.5% | 78.4% | 65.1% | 67.2% |
| Afrikaans-AfriBooms | Gold tok+mor | - | - | - | - | - | - | - | 87.6% | 85.0% | 77.0% | 79.6% |
| Ancient Greek-Perseus | Raw text | 100.0% | 98.8% | 82.2% | 72.2% | 85.7% | 72.2% | 82.7% | 64.0% | 57.0% | 30.2% | 38.2% |
| Ancient Greek-Perseus | Gold tok | - | - | 82.2% | 72.2% | 85.7% | 72.2% | 82.7% | 64.1% | 57.2% | 30.3% | 38.4% |
| Ancient Greek-Perseus | Gold tok+mor | - | - | - | - | - | - | - | 68.7% | 63.9% | 53.1% | 57.2% |
| Ancient Greek-PROIEL | Raw text | 100.0% | 48.0% | 96.0% | 96.2% | 88.6% | 87.2% | 93.2% | 72.2% | 67.6% | 49.9% | 56.0% |
| Ancient Greek-PROIEL | Gold tok | - | - | 96.1% | 96.3% | 88.7% | 87.4% | 93.2% | 77.0% | 72.1% | 54.4% | 60.3% |
| Ancient Greek-PROIEL | Gold tok+mor | - | - | - | - | - | - | - | 80.2% | 76.4% | 66.7% | 70.3% |
| Arabic-PADT | Raw text | 94.6% | 82.1% | 90.4% | 84.0% | 84.2% | 83.8% | 88.5% | 72.7% | 68.1% | 56.2% | 59.2% |
| Arabic-PADT | Gold tok | - | - | 95.6% | 89.0% | 89.2% | 88.8% | 92.9% | 82.0% | 76.8% | 63.6% | 66.3% |
| Arabic-PADT | Gold tok+mor | - | - | - | - | - | - | - | 83.9% | 80.7% | 75.7% | 76.8% |
| Armenian-ArmTDP | Raw text | 99.3% | 97.8% | 92.0% | - | 84.7% | 83.4% | 91.8% | 75.6% | 68.5% | 51.2% | 57.4% |
| Armenian-ArmTDP | Gold tok | - | - | 92.5% | - | 85.2% | 83.8% | 92.4% | 76.9% | 69.7% | 51.5% | 57.9% |
| Armenian-ArmTDP | Gold tok+mor | - | - | - | - | - | - | - | 82.4% | 77.2% | 70.9% | 71.9% |
| Basque-BDT | Raw text | 99.9% | 99.8% | 92.3% | - | 87.3% | 84.8% | 93.5% | 75.0% | 69.9% | 57.4% | 63.2% |
| Basque-BDT | Gold tok | - | - | 92.4% | - | 87.4% | 84.8% | 93.6% | 75.1% | 70.0% | 57.4% | 63.2% |
| Basque-BDT | Gold tok+mor | - | - | - | - | - | - | - | 82.1% | 78.4% | 75.2% | 77.2% |
| Belarusian-HSE | Raw text | 99.8% | 78.7% | 84.3% | 32.0% | 65.3% | 23.9% | 75.0% | 58.6% | 52.9% | 28.8% | 33.1% |
| Belarusian-HSE | Gold tok | - | - | 84.4% | 32.1% | 65.4% | 24.1% | 75.2% | 59.5% | 53.8% | 29.3% | 33.8% |
| Belarusian-HSE | Gold tok+mor | - | - | - | - | - | - | - | 72.9% | 69.6% | 62.4% | 63.3% |
| Bulgarian-BTB | Raw text | 99.9% | 94.2% | 97.6% | 94.3% | 95.4% | 93.8% | 94.6% | 89.1% | 85.1% | 75.4% | 74.0% |
| Bulgarian-BTB | Gold tok | - | - | 97.8% | 94.5% | 95.5% | 93.9% | 94.7% | 90.0% | 85.9% | 76.1% | 74.7% |
| Bulgarian-BTB | Gold tok+mor | - | - | - | - | - | - | - | 92.5% | 89.1% | 84.4% | 84.7% |
| Catalan-AnCora | Raw text | 100.0% | 99.4% | 98.1% | 98.0% | 97.7% | 96.9% | 98.2% | 89.0% | 85.9% | 77.3% | 77.8% |
| Catalan-AnCora | Gold tok | - | - | 98.1% | 98.0% | 97.7% | 97.0% | 98.2% | 89.1% | 86.0% | 77.4% | 77.8% |
| Catalan-AnCora | Gold tok+mor | - | - | - | - | - | - | - | 91.0% | 88.5% | 82.5% | 83.0% |
| Chinese-GSD | Raw text | 90.3% | 99.1% | 84.1% | 84.0% | 89.0% | 82.8% | 90.3% | 61.6% | 57.8% | 50.8% | 54.7% |
| Chinese-GSD | Gold tok | - | - | 92.2% | 92.0% | 98.7% | 90.8% | 100.0% | 74.4% | 69.5% | 62.2% | 67.8% |
| Chinese-GSD | Gold tok+mor | - | - | - | - | - | - | - | 82.6% | 80.5% | 77.5% | 79.6% |
| Chinese-GSDSimp | Raw text | 90.3% | 99.1% | 84.2% | 84.1% | 89.0% | 82.8% | 90.3% | 62.6% | 58.7% | 51.3% | 55.2% |
| Chinese-GSDSimp | Gold tok | - | - | 92.0% | 91.8% | 98.6% | 90.5% | 100.0% | 74.1% | 69.0% | 61.8% | 67.1% |
| Chinese-GSDSimp | Gold tok+mor | - | - | - | - | - | - | - | 82.7% | 80.4% | 77.5% | 79.6% |
| Classical Chinese-Kyoto | Raw text | 99.5% | 41.7% | 89.9% | 89.2% | 92.3% | 86.7% | 99.5% | 68.9% | 62.9% | 59.1% | 61.3% |
| Classical Chinese-Kyoto | Gold tok | - | - | 92.0% | 91.3% | 93.6% | 89.2% | 100.0% | 81.0% | 74.7% | 71.0% | 73.5% |
| Classical Chinese-Kyoto | Gold tok+mor | - | - | - | - | - | - | - | 90.0% | 85.9% | 84.6% | 85.0% |
| Coptic-Scriptorium | Raw text | 70.7% | 27.7% | 67.1% | 64.6% | 50.2% | 46.3% | 68.7% | 43.8% | 41.8% | 12.7% | 32.0% |
| Coptic-Scriptorium | Gold tok | - | - | 94.0% | 88.5% | 70.6% | 62.4% | 96.0% | 85.3% | 80.4% | 29.9% | 69.8% |
| Coptic-Scriptorium | Gold tok+mor | - | - | - | - | - | - | - | 88.9% | 85.7% | 75.8% | 78.1% |
| Croatian-SET | Raw text | 100.0% | 94.4% | 96.5% | 90.3% | 90.9% | 89.9% | 95.3% | 83.7% | 77.7% | 64.8% | 69.3% |
| Croatian-SET | Gold tok | - | - | 96.5% | 90.4% | 91.1% | 90.1% | 95.3% | 84.2% | 78.1% | 65.3% | 69.7% |
| Croatian-SET | Gold tok+mor | - | - | - | - | - | - | - | 86.8% | 82.2% | 77.2% | 78.9% |
| Czech-CAC | Raw text | 100.0% | 99.7% | 98.3% | 91.0% | 90.0% | 89.6% | 97.0% | 86.3% | 82.8% | 70.5% | 77.0% |
| Czech-CAC | Gold tok | - | - | 98.3% | 91.0% | 90.0% | 89.6% | 97.0% | 86.4% | 82.9% | 70.5% | 77.1% |
| Czech-CAC | Gold tok+mor | - | - | - | - | - | - | - | 89.7% | 87.3% | 84.2% | 85.0% |
| Czech-CLTT | Raw text | 99.7% | 97.4% | 97.3% | 87.3% | 87.4% | 87.1% | 96.1% | 78.9% | 75.4% | 60.0% | 68.9% |
| Czech-CLTT | Gold tok | - | - | 97.6% | 87.5% | 87.7% | 87.4% | 96.4% | 79.6% | 76.1% | 60.4% | 69.4% |
| Czech-CLTT | Gold tok+mor | - | - | - | - | - | - | - | 82.7% | 79.6% | 74.7% | 75.3% |
| Czech-FicTree | Raw text | 100.0% | 99.0% | 97.1% | 89.9% | 90.8% | 89.4% | 97.1% | 86.3% | 82.0% | 68.7% | 74.3% |
| Czech-FicTree | Gold tok | - | - | 97.1% | 90.0% | 90.8% | 89.5% | 97.1% | 86.4% | 82.1% | 68.8% | 74.4% |
| Czech-FicTree | Gold tok+mor | - | - | - | - | - | - | - | 90.5% | 87.7% | 83.3% | 84.1% |
| Czech-PDT | Raw text | 99.9% | 93.3% | 98.2% | 92.7% | 92.4% | 91.9% | 97.8% | 87.0% | 84.0% | 74.4% | 79.4% |
| Czech-PDT | Gold tok | - | - | 98.3% | 92.9% | 92.5% | 92.0% | 97.9% | 87.8% | 84.7% | 75.0% | 80.0% |
| Czech-PDT | Gold tok+mor | - | - | - | - | - | - | - | 90.3% | 88.2% | 85.6% | 86.1% |
| Danish-DDT | Raw text | 99.8% | 89.8% | 95.4% | - | 94.8% | 93.4% | 94.7% | 79.3% | 76.0% | 66.4% | 66.8% |
| Danish-DDT | Gold tok | - | - | 95.6% | - | 95.0% | 93.6% | 94.9% | 80.2% | 76.8% | 67.2% | 67.5% |
| Danish-DDT | Gold tok+mor | - | - | - | - | - | - | - | 84.8% | 82.4% | 77.4% | 79.1% |
| Dutch-Alpino | Raw text | 99.8% | 88.6% | 94.1% | 91.5% | 93.3% | 90.7% | 95.1% | 82.4% | 78.2% | 62.3% | 65.1% |
| Dutch-Alpino | Gold tok | - | - | 94.3% | 91.7% | 93.5% | 90.9% | 95.3% | 83.3% | 79.0% | 63.3% | 66.1% |
| Dutch-Alpino | Gold tok+mor | - | - | - | - | - | - | - | 86.9% | 83.2% | 74.8% | 76.1% |
| Dutch-LassySmall | Raw text | 99.8% | 75.4% | 94.1% | 91.8% | 93.7% | 91.0% | 95.5% | 79.0% | 75.0% | 62.6% | 63.2% |
| Dutch-LassySmall | Gold tok | - | - | 94.2% | 92.1% | 94.2% | 91.3% | 95.7% | 82.4% | 78.0% | 66.3% | 67.0% |
| Dutch-LassySmall | Gold tok+mor | - | - | - | - | - | - | - | 87.6% | 84.5% | 78.6% | 79.3% |
| English-EWT | Raw text | 98.9% | 77.4% | 93.3% | 92.8% | 94.2% | 91.3% | 95.5% | 80.2% | 77.0% | 67.7% | 69.5% |
| English-EWT | Gold tok | - | - | 94.4% | 93.9% | 95.4% | 92.5% | 96.4% | 84.4% | 81.1% | 71.6% | 73.6% |
| English-EWT | Gold tok+mor | - | - | - | - | - | - | - | 88.1% | 86.2% | 82.4% | 83.1% |
| English-GUM | Raw text | 99.7% | 82.3% | 93.8% | 93.3% | 94.3% | 92.2% | 94.9% | 80.2% | 76.2% | 64.8% | 65.1% |
| English-GUM | Gold tok | - | - | 94.1% | 93.7% | 94.6% | 92.5% | 95.2% | 82.0% | 77.9% | 66.2% | 66.6% |
| English-GUM | Gold tok+mor | - | - | - | - | - | - | - | 86.5% | 84.4% | 78.0% | 79.0% |
| English-LinES | Raw text | 99.9% | 87.5% | 94.9% | 92.7% | 94.9% | 90.3% | 97.1% | 80.4% | 75.9% | 67.0% | 69.5% |
| English-LinES | Gold tok | - | - | 95.0% | 92.8% | 95.0% | 90.4% | 97.1% | 81.2% | 76.6% | 67.7% | 70.2% |
| English-LinES | Gold tok+mor | - | - | - | - | - | - | - | 85.5% | 82.3% | 78.1% | 79.8% |
| English-ParTUT | Raw text | 99.7% | 100.0% | 93.6% | 93.2% | 93.4% | 91.7% | 96.7% | 83.9% | 80.4% | 69.0% | 71.4% |
| English-ParTUT | Gold tok | - | - | 93.8% | 93.5% | 93.6% | 91.9% | 97.0% | 84.2% | 80.6% | 69.1% | 71.5% |
| English-ParTUT | Gold tok+mor | - | - | - | - | - | - | - | 86.8% | 85.4% | 79.0% | 80.1% |
| Estonian-EDT | Raw text | 100.0% | 91.6% | 95.6% | 96.8% | 93.4% | 91.8% | 90.6% | 79.5% | 75.8% | 68.3% | 64.7% |
| Estonian-EDT | Gold tok | - | - | 95.7% | 96.8% | 93.5% | 91.8% | 90.6% | 80.3% | 76.5% | 69.0% | 65.3% |
| Estonian-EDT | Gold tok+mor | - | - | - | - | - | - | - | 85.5% | 83.3% | 80.4% | 81.5% |
| Estonian-EWT | Raw text | 99.1% | 67.0% | 83.2% | 85.6% | 79.5% | 76.2% | 79.8% | 60.1% | 51.2% | 38.8% | 36.7% |
| Estonian-EWT | Gold tok | - | - | 84.0% | 86.3% | 80.2% | 77.0% | 80.4% | 62.6% | 53.2% | 39.8% | 37.6% |
| Estonian-EWT | Gold tok+mor | - | - | - | - | - | - | - | 74.1% | 69.9% | 64.6% | 65.7% |
| Finnish-FTB | Raw text | 99.9% | 86.8% | 91.5% | 90.8% | 92.5% | 88.9% | 88.5% | 79.4% | 74.9% | 64.1% | 60.9% |
| Finnish-FTB | Gold tok | - | - | 91.8% | 91.0% | 92.7% | 89.2% | 88.6% | 81.1% | 76.6% | 65.9% | 62.6% |
| Finnish-FTB | Gold tok+mor | - | - | - | - | - | - | - | 89.9% | 87.6% | 82.9% | 84.2% |
| Finnish-TDT | Raw text | 99.7% | 88.6% | 94.3% | 95.4% | 92.0% | 90.8% | 86.9% | 80.5% | 76.8% | 68.6% | 62.8% |
| Finnish-TDT | Gold tok | - | - | 94.7% | 95.8% | 92.4% | 91.2% | 87.2% | 81.8% | 78.1% | 69.5% | 63.6% |
| Finnish-TDT | Gold tok+mor | - | - | - | - | - | - | - | 86.8% | 84.7% | 81.6% | 82.5% |
| French-GSD | Raw text | 98.8% | 93.6% | 95.8% | - | 95.5% | 94.5% | 96.6% | 87.1% | 84.3% | 74.4% | 76.3% |
| French-GSD | Gold tok | - | - | 97.1% | - | 96.6% | 95.6% | 97.8% | 89.0% | 86.3% | 76.0% | 77.7% |
| French-GSD | Gold tok+mor | - | - | - | - | - | - | - | 91.1% | 89.2% | 83.9% | 84.2% |
| French-ParTUT | Raw text | 99.4% | 100.0% | 94.6% | 94.2% | 91.9% | 90.6% | 95.0% | 87.4% | 83.5% | 66.4% | 71.5% |
| French-ParTUT | Gold tok | - | - | 95.3% | 94.8% | 92.6% | 91.3% | 95.6% | 88.0% | 84.3% | 67.4% | 72.0% |
| French-ParTUT | Gold tok+mor | - | - | - | - | - | - | - | 91.0% | 89.0% | 82.1% | 83.4% |
| French-Sequoia | Raw text | 99.1% | 87.5% | 96.1% | - | 95.0% | 94.1% | 96.9% | 84.8% | 82.1% | 72.5% | 75.1% |
| French-Sequoia | Gold tok | - | - | 97.1% | - | 95.9% | 95.0% | 97.8% | 86.8% | 84.1% | 74.5% | 76.8% |
| French-Sequoia | Gold tok+mor | - | - | - | - | - | - | - | 89.7% | 88.1% | 83.8% | 84.0% |
| French-Spoken | Raw text | 99.1% | 21.1% | 91.9% | 96.3% | - | 89.6% | 94.5% | 69.2% | 63.3% | 51.6% | 51.8% |
| French-Spoken | Gold tok | - | - | 93.1% | 97.2% | - | 90.6% | 95.5% | 76.1% | 69.9% | 59.4% | 59.4% |
| French-Spoken | Gold tok+mor | - | - | - | - | - | - | - | 80.7% | 76.5% | 67.5% | 68.6% |
| Galician-CTG | Raw text | 99.2% | 97.2% | 96.3% | 95.8% | 99.0% | 95.4% | 96.2% | 79.3% | 76.2% | 62.5% | 65.4% |
| Galician-CTG | Gold tok | - | - | 97.0% | 96.5% | 99.8% | 96.2% | 96.9% | 80.8% | 77.7% | 64.3% | 67.2% |
| Galician-CTG | Gold tok+mor | - | - | - | - | - | - | - | 83.0% | 80.7% | 69.4% | 74.1% |
| Galician-TreeGal | Raw text | 98.7% | 88.0% | 91.1% | 87.3% | 89.5% | 86.6% | 92.5% | 72.1% | 66.6% | 49.8% | 52.2% |
| Galician-TreeGal | Gold tok | - | - | 92.2% | 88.1% | 90.4% | 87.4% | 93.6% | 75.1% | 69.1% | 52.4% | 55.2% |
| Galician-TreeGal | Gold tok+mor | - | - | - | - | - | - | - | 81.7% | 77.5% | 69.4% | 70.7% |
| German-GSD | Raw text | 99.6% | 80.9% | 91.7% | 79.5% | 69.8% | 62.9% | 95.4% | 78.1% | 72.7% | 34.5% | 61.4% |
| German-GSD | Gold tok | - | - | 92.1% | 79.8% | 70.2% | 63.4% | 95.8% | 80.6% | 75.0% | 35.9% | 63.6% |
| German-GSD | Gold tok+mor | - | - | - | - | - | - | - | 85.5% | 81.2% | 72.5% | 75.3% |
| German-HDT | Raw text | 99.9% | 92.6% | 97.8% | 97.4% | 91.4% | 91.0% | 94.5% | 94.0% | 92.3% | 77.0% | 81.4% |
| German-HDT | Gold tok | - | - | 97.9% | 97.5% | 91.5% | 91.1% | 94.6% | 94.6% | 92.9% | 77.5% | 82.0% |
| German-HDT | Gold tok+mor | - | - | - | - | - | - | - | 95.5% | 94.4% | 91.5% | 91.7% |
| Gothic-PROIEL | Raw text | 100.0% | 31.1% | 94.3% | 94.8% | 87.4% | 85.5% | 92.6% | 68.6% | 62.0% | 48.8% | 54.6% |
| Gothic-PROIEL | Gold tok | - | - | 94.8% | 95.2% | 87.6% | 85.9% | 92.7% | 76.8% | 70.0% | 56.2% | 61.7% |
| Gothic-PROIEL | Gold tok+mor | - | - | - | - | - | - | - | 80.0% | 76.0% | 69.1% | 72.5% |
| Greek-GDT | Raw text | 99.9% | 90.2% | 95.7% | 95.7% | 90.3% | 89.0% | 94.6% | 86.5% | 83.0% | 66.2% | 69.6% |
| Greek-GDT | Gold tok | - | - | 95.9% | 95.9% | 90.5% | 89.2% | 94.7% | 87.2% | 83.7% | 66.8% | 70.2% |
| Greek-GDT | Gold tok+mor | - | - | - | - | - | - | - | 89.7% | 87.9% | 81.6% | 82.7% |
| Hebrew-HTB | Raw text | 85.0% | 99.4% | 80.5% | 80.5% | 78.7% | 77.7% | 81.6% | 61.7% | 58.3% | 44.6% | 47.5% |
| Hebrew-HTB | Gold tok | - | - | 94.9% | 94.9% | 92.7% | 91.5% | 95.4% | 83.6% | 79.6% | 64.3% | 67.1% |
| Hebrew-HTB | Gold tok+mor | - | - | - | - | - | - | - | 87.0% | 84.9% | 78.4% | 78.8% |
| Hindi-HDTB | Raw text | 100.0% | 98.9% | 95.9% | 94.9% | 90.4% | 87.8% | 98.1% | 91.3% | 87.2% | 69.2% | 80.1% |
| Hindi-HDTB | Gold tok | - | - | 95.9% | 95.0% | 90.4% | 87.8% | 98.1% | 91.3% | 87.2% | 69.3% | 80.2% |
| Hindi-HDTB | Gold tok+mor | - | - | - | - | - | - | - | 93.8% | 90.8% | 85.4% | 86.6% |
| Hungarian-Szeged | Raw text | 99.8% | 95.9% | 90.6% | - | 88.1% | 86.4% | 88.5% | 72.8% | 67.2% | 53.7% | 57.9% |
| Hungarian-Szeged | Gold tok | - | - | 90.7% | - | 88.2% | 86.5% | 88.7% | 73.3% | 67.6% | 53.9% | 58.1% |
| Hungarian-Szeged | Gold tok+mor | - | - | - | - | - | - | - | 80.5% | 77.6% | 72.7% | 76.6% |
| Indonesian-GSD | Raw text | 100.0% | 94.1% | 93.0% | 92.2% | 93.9% | 87.1% | 92.2% | 81.0% | 74.6% | 63.6% | 62.9% |
| Indonesian-GSD | Gold tok | - | - | 93.0% | 92.2% | 93.9% | 87.1% | 92.2% | 81.2% | 74.8% | 63.9% | 63.2% |
| Indonesian-GSD | Gold tok+mor | - | - | - | - | - | - | - | 84.0% | 79.8% | 76.4% | 78.3% |
| Irish-IDT | Raw text | 99.7% | 97.5% | 90.0% | 88.9% | 77.7% | 74.5% | 87.9% | 76.8% | 66.4% | 39.4% | 47.9% |
| Irish-IDT | Gold tok | - | - | 90.2% | 89.2% | 78.0% | 74.7% | 88.2% | 77.1% | 66.8% | 39.5% | 48.1% |
| Irish-IDT | Gold tok+mor | - | - | - | - | - | - | - | 80.9% | 73.9% | 62.7% | 64.9% |
| Italian-ISDT | Raw text | 99.8% | 98.8% | 97.2% | 97.0% | 97.1% | 96.2% | 97.4% | 88.8% | 86.2% | 76.8% | 76.7% |
| Italian-ISDT | Gold tok | - | - | 97.3% | 97.2% | 97.2% | 96.3% | 97.5% | 89.1% | 86.6% | 77.3% | 77.2% |
| Italian-ISDT | Gold tok+mor | - | - | - | - | - | - | - | 91.3% | 89.7% | 84.2% | 84.5% |
| Italian-ParTUT | Raw text | 99.7% | 100.0% | 97.0% | 96.4% | 96.3% | 95.2% | 96.5% | 87.5% | 84.3% | 73.2% | 72.4% |
| Italian-ParTUT | Gold tok | - | - | 97.1% | 96.6% | 96.5% | 95.3% | 96.7% | 87.4% | 84.3% | 73.1% | 72.3% |
| Italian-ParTUT | Gold tok+mor | - | - | - | - | - | - | - | 89.7% | 87.6% | 79.8% | 80.7% |
| Italian-PoSTWITA | Raw text | 99.5% | 30.5% | 94.0% | 93.7% | 94.4% | 92.4% | 95.1% | 74.4% | 69.4% | 56.8% | 57.4% |
| Italian-PoSTWITA | Gold tok | - | - | 94.6% | 94.2% | 94.9% | 92.9% | 95.6% | 80.1% | 74.8% | 63.8% | 64.6% |
| Italian-PoSTWITA | Gold tok+mor | - | - | - | - | - | - | - | 84.4% | 80.0% | 73.7% | 74.3% |
| Italian-TWITTIRO | Raw text | 99.1% | 36.8% | 90.4% | 89.7% | 89.8% | 86.9% | 90.2% | 71.9% | 65.3% | 47.8% | 48.1% |
| Italian-TWITTIRO | Gold tok | - | - | 91.5% | 90.7% | 91.0% | 87.9% | 91.5% | 76.3% | 69.4% | 52.1% | 52.8% |
| Italian-TWITTIRO | Gold tok+mor | - | - | - | - | - | - | - | 84.1% | 78.7% | 69.9% | 70.8% |
| Italian-VIT | Raw text | 99.7% | 94.7% | 96.1% | 95.0% | 95.9% | 93.5% | 96.8% | 83.5% | 79.4% | 67.2% | 68.5% |
| Italian-VIT | Gold tok | - | - | 96.4% | 95.3% | 96.1% | 93.8% | 97.0% | 84.2% | 80.1% | 67.9% | 69.2% |
| Italian-VIT | Gold tok+mor | - | - | - | - | - | - | - | 87.3% | 84.4% | 77.4% | 78.3% |
| Japanese-GSD | Raw text | 90.6% | 94.7% | 88.0% | 87.7% | 90.6% | 87.7% | 89.8% | 75.2% | 73.7% | 60.6% | 62.6% |
| Japanese-GSD | Gold tok | - | - | 96.9% | 96.4% | 100.0% | 96.4% | 99.1% | 92.7% | 90.7% | 80.6% | 82.8% |
| Japanese-GSD | Gold tok+mor | - | - | - | - | - | - | - | 94.8% | 93.8% | 87.0% | 87.1% |
| Korean-GSD | Raw text | 99.9% | 93.9% | 93.6% | 81.7% | 99.6% | 79.5% | 87.1% | 69.6% | 61.5% | 54.1% | 50.8% |
| Korean-GSD | Gold tok | - | - | 93.7% | 81.9% | 99.7% | 79.7% | 87.2% | 70.3% | 62.1% | 54.8% | 51.4% |
| Korean-GSD | Gold tok+mor | - | - | - | - | - | - | - | 72.5% | 65.5% | 60.4% | 61.6% |
| Korean-Kaist | Raw text | 100.0% | 100.0% | 93.3% | 80.1% | - | 80.1% | 88.5% | 77.9% | 70.6% | 61.9% | 58.1% |
| Korean-Kaist | Gold tok | - | - | 93.4% | 80.1% | - | 80.1% | 88.5% | 78.0% | 70.7% | 62.0% | 58.1% |
| Korean-Kaist | Gold tok+mor | - | - | - | - | - | - | - | 80.3% | 73.7% | 67.8% | 68.4% |
| Latin-ITTB | Raw text | 100.0% | 92.4% | 97.1% | 93.0% | 93.4% | 91.4% | 98.0% | 83.4% | 80.1% | 70.6% | 75.5% |
| Latin-ITTB | Gold tok | - | - | 97.1% | 93.0% | 93.3% | 91.4% | 98.0% | 83.9% | 80.5% | 70.7% | 75.5% |
| Latin-ITTB | Gold tok+mor | - | - | - | - | - | - | - | 87.8% | 85.8% | 81.9% | 83.0% |
| Latin-PROIEL | Raw text | 99.9% | 36.8% | 94.5% | 94.7% | 86.7% | 85.6% | 94.5% | 65.9% | 60.1% | 47.7% | 54.4% |
| Latin-PROIEL | Gold tok | - | - | 94.7% | 94.8% | 87.2% | 86.2% | 94.7% | 73.4% | 67.3% | 54.9% | 61.3% |
| Latin-PROIEL | Gold tok+mor | - | - | - | - | - | - | - | 77.2% | 73.4% | 67.1% | 70.3% |
| Latin-Perseus | Raw text | 100.0% | 98.5% | 83.3% | 67.2% | 72.1% | 67.2% | 78.0% | 57.7% | 47.1% | 29.4% | 31.9% |
| Latin-Perseus | Gold tok | - | - | 83.3% | 67.2% | 72.1% | 67.2% | 77.9% | 57.9% | 47.2% | 29.4% | 31.9% |
| Latin-Perseus | Gold tok+mor | - | - | - | - | - | - | - | 67.6% | 61.8% | 55.9% | 59.1% |
| Latvian-LVTB | Raw text | 99.3% | 98.7% | 93.5% | 84.3% | 89.5% | 83.9% | 92.7% | 78.8% | 74.2% | 62.1% | 65.7% |
| Latvian-LVTB | Gold tok | - | - | 94.0% | 84.8% | 90.1% | 84.5% | 93.3% | 79.7% | 75.1% | 62.9% | 66.5% |
| Latvian-LVTB | Gold tok+mor | - | - | - | - | - | - | - | 86.4% | 83.5% | 79.8% | 81.0% |
| Lithuanian-ALKSNIS | Raw text | 99.9% | 87.9% | 90.4% | 80.7% | 82.3% | 80.4% | 88.8% | 69.0% | 62.5% | 48.8% | 52.0% |
| Lithuanian-ALKSNIS | Gold tok | - | - | 90.5% | 80.9% | 82.5% | 80.6% | 88.9% | 69.8% | 63.4% | 49.4% | 52.5% |
| Lithuanian-ALKSNIS | Gold tok+mor | - | - | - | - | - | - | - | 77.2% | 73.9% | 70.6% | 71.6% |
| Lithuanian-HSE | Raw text | 97.3% | 97.3% | 73.2% | 72.1% | 68.2% | 62.7% | 71.1% | 46.9% | 33.5% | 21.6% | 22.1% |
| Lithuanian-HSE | Gold tok | - | - | 74.1% | 73.1% | 69.2% | 63.6% | 72.0% | 48.3% | 34.7% | 21.9% | 22.3% |
| Lithuanian-HSE | Gold tok+mor | - | - | - | - | - | - | - | 56.9% | 48.1% | 42.6% | 44.4% |
| Maltese-MUDT | Raw text | 99.8% | 86.3% | 93.8% | 93.5% | - | 93.2% | - | 77.7% | 71.8% | 58.4% | 61.9% |
| Maltese-MUDT | Gold tok | - | - | 93.9% | 93.7% | - | 93.4% | - | 78.0% | 72.2% | 58.6% | 62.1% |
| Maltese-MUDT | Gold tok+mor | - | - | - | - | - | - | - | 82.2% | 77.9% | 68.4% | 69.7% |
| Marathi-UFAL | Raw text | 90.2% | 92.6% | 71.5% | - | 60.8% | 58.2% | 76.2% | 60.5% | 49.5% | 23.9% | 30.7% |
| Marathi-UFAL | Gold tok | - | - | 77.7% | - | 63.4% | 60.4% | 76.5% | 68.5% | 54.9% | 26.0% | 31.0% |
| Marathi-UFAL | Gold tok+mor | - | - | - | - | - | - | - | 77.9% | 67.7% | 60.7% | 63.1% |
| North Sami-Giella | Raw text | 99.9% | 98.8% | 87.8% | 89.4% | 82.5% | 78.4% | 82.0% | 64.7% | 57.9% | 46.7% | 43.2% |
| North Sami-Giella | Gold tok | - | - | 87.9% | 89.6% | 82.6% | 78.5% | 82.1% | 64.9% | 58.2% | 46.9% | 43.5% |
| North Sami-Giella | Gold tok+mor | - | - | - | - | - | - | - | 81.4% | 78.7% | 74.2% | 77.2% |
| Norwegian-Bokmaal | Raw text | 100.0% | 96.5% | 96.6% | - | 95.4% | 94.1% | 96.9% | 87.2% | 84.4% | 75.3% | 77.1% |
| Norwegian-Bokmaal | Gold tok | - | - | 96.7% | - | 95.4% | 94.2% | 97.0% | 87.5% | 84.7% | 75.6% | 77.5% |
| Norwegian-Bokmaal | Gold tok+mor | - | - | - | - | - | - | - | 92.0% | 90.2% | 86.1% | 86.9% |
| Norwegian-Nynorsk | Raw text | 99.9% | 94.1% | 96.0% | - | 94.9% | 93.6% | 96.3% | 85.7% | 82.8% | 72.8% | 74.5% |
| Norwegian-Nynorsk | Gold tok | - | - | 96.2% | - | 95.0% | 93.7% | 96.4% | 86.4% | 83.5% | 73.6% | 75.2% |
| Norwegian-Nynorsk | Gold tok+mor | - | - | - | - | - | - | - | 91.1% | 89.2% | 84.7% | 85.8% |
| Norwegian-NynorskLIA | Raw text | 99.8% | 99.5% | 93.7% | - | 93.2% | 90.4% | 96.2% | 74.2% | 68.9% | 55.9% | 60.1% |
| Norwegian-NynorskLIA | Gold tok | - | - | 93.8% | - | 93.3% | 90.5% | 96.4% | 74.4% | 69.1% | 56.0% | 60.3% |
| Norwegian-NynorskLIA | Gold tok+mor | - | - | - | - | - | - | - | 81.1% | 76.8% | 69.1% | 71.3% |
| Old Church Slavonic-PROIEL | Raw text | 100.0% | 41.4% | 93.5% | 93.7% | 86.8% | 85.5% | 90.9% | 71.2% | 65.3% | 54.8% | 59.5% |
| Old Church Slavonic-PROIEL | Gold tok | - | - | 93.8% | 94.0% | 87.4% | 86.2% | 91.0% | 79.5% | 73.2% | 62.4% | 66.0% |
| Old Church Slavonic-PROIEL | Gold tok+mor | - | - | - | - | - | - | - | 85.1% | 81.3% | 76.8% | 79.6% |
| Old French-SRCMF | Raw text | 99.9% | 100.0% | 94.2% | 93.8% | 96.0% | 93.3% | - | 85.5% | 79.3% | 70.9% | 74.6% |
| Old French-SRCMF | Gold tok | - | - | 94.3% | 93.9% | 96.1% | 93.4% | - | 85.6% | 79.4% | 71.0% | 74.7% |
| Old French-SRCMF | Gold tok+mor | - | - | - | - | - | - | - | 88.8% | 84.4% | 78.4% | 79.7% |
| Old Russian-TOROT | Raw text | 100.0% | 29.6% | 89.9% | 90.0% | 81.8% | 79.6% | 81.2% | 63.9% | 56.9% | 42.9% | 44.2% |
| Old Russian-TOROT | Gold tok | - | - | 90.5% | 90.5% | 82.5% | 80.5% | 81.3% | 73.4% | 65.9% | 51.5% | 50.8% |
| Old Russian-TOROT | Gold tok+mor | - | - | - | - | - | - | - | 80.5% | 76.4% | 70.4% | 73.2% |
| Persian-Seraji | Raw text | 99.7% | 98.8% | 96.0% | 95.9% | 96.1% | 95.4% | 93.6% | 83.6% | 79.6% | 72.8% | 70.1% |
| Persian-Seraji | Gold tok | - | - | 96.3% | 96.3% | 96.4% | 95.7% | 93.9% | 84.3% | 80.2% | 73.3% | 70.5% |
| Persian-Seraji | Gold tok+mor | - | - | - | - | - | - | - | 87.2% | 84.3% | 80.0% | 80.8% |
| Polish-LFG | Raw text | 99.8% | 99.7% | 96.7% | 87.2% | 89.1% | 86.5% | 94.5% | 90.9% | 87.4% | 74.4% | 78.5% |
| Polish-LFG | Gold tok | - | - | 96.9% | 87.3% | 89.2% | 86.7% | 94.6% | 91.3% | 87.8% | 74.7% | 78.8% |
| Polish-LFG | Gold tok+mor | - | - | - | - | - | - | - | 96.2% | 94.8% | 92.9% | 93.1% |
| Polish-PDB | Raw text | 99.8% | 97.3% | 97.1% | 88.1% | 88.6% | 87.5% | 96.0% | 86.5% | 82.7% | 68.8% | 75.4% |
| Polish-PDB | Gold tok | - | - | 97.2% | 88.2% | 88.8% | 87.7% | 96.1% | 87.0% | 83.2% | 69.1% | 75.8% |
| Polish-PDB | Gold tok+mor | - | - | - | - | - | - | - | 90.5% | 88.8% | 86.0% | 86.3% |
| Portuguese-Bosque | Raw text | 99.5% | 87.8% | 95.9% | - | 94.5% | 92.7% | 96.7% | 85.9% | 81.9% | 68.0% | 71.8% |
| Portuguese-Bosque | Gold tok | - | - | 96.4% | - | 95.0% | 93.2% | 97.2% | 87.2% | 83.1% | 69.0% | 73.0% |
| Portuguese-Bosque | Gold tok+mor | - | - | - | - | - | - | - | 89.3% | 86.3% | 79.5% | 80.5% |
| Portuguese-GSD | Raw text | 99.8% | 97.1% | 97.0% | 97.0% | 99.7% | 97.0% | 98.5% | 88.0% | 85.9% | 77.8% | 78.5% |
| Portuguese-GSD | Gold tok | - | - | 97.2% | 97.2% | 99.9% | 97.2% | 98.7% | 88.4% | 86.3% | 78.2% | 78.8% |
| Portuguese-GSD | Gold tok+mor | - | - | - | - | - | - | - | 90.9% | 89.5% | 83.7% | 84.3% |
| Romanian-Nonstandard | Raw text | 98.2% | 96.7% | 94.2% | 89.4% | 88.4% | 87.1% | 92.4% | 82.3% | 77.1% | 59.0% | 65.7% |
| Romanian-Nonstandard | Gold tok | - | - | 95.9% | 91.0% | 89.9% | 88.5% | 94.0% | 84.9% | 79.4% | 60.9% | 67.3% |
| Romanian-Nonstandard | Gold tok+mor | - | - | - | - | - | - | - | 87.8% | 82.8% | 75.2% | 77.0% |
| Romanian-RRT | Raw text | 99.7% | 95.3% | 96.7% | 95.9% | 96.1% | 95.7% | 96.6% | 85.3% | 80.0% | 71.5% | 71.6% |
| Romanian-RRT | Gold tok | - | - | 96.9% | 96.2% | 96.4% | 96.0% | 96.8% | 86.0% | 80.6% | 72.0% | 72.1% |
| Romanian-RRT | Gold tok+mor | - | - | - | - | - | - | - | 87.9% | 83.1% | 76.9% | 77.9% |
| Russian-GSD | Raw text | 99.5% | 96.2% | 95.0% | 94.7% | 85.4% | 84.3% | 92.3% | 82.3% | 77.4% | 62.1% | 67.4% |
| Russian-GSD | Gold tok | - | - | 95.4% | 95.1% | 85.8% | 84.7% | 92.7% | 83.5% | 78.6% | 62.9% | 68.3% |
| Russian-GSD | Gold tok+mor | - | - | - | - | - | - | - | 87.0% | 83.9% | 81.0% | 81.5% |
| Russian-SynTagRus | Raw text | 99.6% | 98.8% | 97.8% | - | 93.5% | 93.2% | 96.5% | 87.6% | 85.0% | 77.0% | 79.4% |
| Russian-SynTagRus | Gold tok | - | - | 98.2% | - | 93.9% | 93.5% | 96.9% | 88.3% | 85.7% | 77.5% | 79.9% |
| Russian-SynTagRus | Gold tok+mor | - | - | - | - | - | - | - | 90.3% | 89.0% | 86.9% | 87.3% |
| Russian-Taiga | Raw text | 97.6% | 76.0% | 88.3% | 91.5% | 77.0% | 71.0% | 84.9% | 65.1% | 57.7% | 38.2% | 43.8% |
| Russian-Taiga | Gold tok | - | - | 90.4% | 93.8% | 79.2% | 72.9% | 87.0% | 69.2% | 61.3% | 40.7% | 46.7% |
| Russian-Taiga | Gold tok+mor | - | - | - | - | - | - | - | 75.4% | 70.7% | 64.7% | 66.1% |
| Scottish Gaelic-ARCOSG | Raw text | 99.6% | 54.7% | 90.5% | 80.1% | 84.7% | 79.5% | 92.1% | 73.9% | 66.1% | 47.7% | 52.4% |
| Scottish Gaelic-ARCOSG | Gold tok | - | - | 91.1% | 80.7% | 85.1% | 80.0% | 92.5% | 78.5% | 70.4% | 52.2% | 58.1% |
| Scottish Gaelic-ARCOSG | Gold tok+mor | - | - | - | - | - | - | - | 82.6% | 78.0% | 69.4% | 71.3% |
| Serbian-SET | Raw text | 100.0% | 93.0% | 97.1% | 90.7% | 91.1% | 90.5% | 95.1% | 85.7% | 81.8% | 69.7% | 73.8% |
| Serbian-SET | Gold tok | - | - | 97.2% | 90.8% | 91.2% | 90.7% | 95.1% | 86.3% | 82.4% | 70.3% | 74.4% |
| Serbian-SET | Gold tok+mor | - | - | - | - | - | - | - | 89.2% | 86.4% | 82.6% | 83.7% |
| Slovak-SNK | Raw text | 100.0% | 85.3% | 92.9% | 77.0% | 80.3% | 76.7% | 86.6% | 81.0% | 76.3% | 56.1% | 60.7% |
| Slovak-SNK | Gold tok | - | - | 93.0% | 77.2% | 80.5% | 76.8% | 86.6% | 82.5% | 77.8% | 57.2% | 61.5% |
| Slovak-SNK | Gold tok+mor | - | - | - | - | - | - | - | 88.9% | 86.7% | 83.7% | 84.5% |
| Slovenian-SSJ | Raw text | 98.0% | 68.0% | 94.2% | 86.2% | 86.5% | 85.7% | 93.4% | 79.6% | 76.4% | 62.8% | 68.2% |
| Slovenian-SSJ | Gold tok | - | - | 96.2% | 88.3% | 88.7% | 87.8% | 95.3% | 85.2% | 81.8% | 67.2% | 72.8% |
| Slovenian-SSJ | Gold tok+mor | - | - | - | - | - | - | - | 92.1% | 90.6% | 87.5% | 87.9% |
| Slovenian-SST | Raw text | 99.8% | 23.1% | 87.5% | 79.7% | 79.8% | 76.8% | 90.8% | 54.1% | 47.0% | 33.9% | 38.1% |
| Slovenian-SST | Gold tok | - | - | 88.7% | 80.3% | 80.5% | 77.9% | 91.1% | 64.5% | 56.9% | 43.0% | 48.7% |
| Slovenian-SST | Gold tok+mor | - | - | - | - | - | - | - | 76.0% | 70.7% | 64.3% | 66.6% |
| Spanish-AnCora | Raw text | 100.0% | 98.3% | 98.3% | 98.1% | 98.1% | 97.4% | 98.5% | 88.2% | 85.1% | 77.0% | 77.5% |
| Spanish-AnCora | Gold tok | - | - | 98.4% | 98.2% | 98.2% | 97.4% | 98.5% | 88.4% | 85.3% | 77.2% | 77.7% |
| Spanish-AnCora | Gold tok+mor | - | - | - | - | - | - | - | 90.2% | 87.6% | 81.4% | 82.3% |
| Spanish-GSD | Raw text | 99.8% | 94.5% | 95.5% | - | 96.2% | 93.7% | 95.9% | 85.4% | 82.0% | 68.6% | 69.3% |
| Spanish-GSD | Gold tok | - | - | 95.7% | - | 96.5% | 94.0% | 96.1% | 86.0% | 82.5% | 69.1% | 69.7% |
| Spanish-GSD | Gold tok+mor | - | - | - | - | - | - | - | 88.4% | 85.8% | 78.7% | 79.6% |
| Swedish-LinES | Raw text | 100.0% | 87.2% | 94.5% | 92.1% | 88.5% | 85.3% | 95.1% | 80.4% | 75.8% | 60.7% | 68.1% |
| Swedish-LinES | Gold tok | - | - | 94.7% | 92.2% | 88.6% | 85.4% | 95.2% | 81.2% | 76.5% | 61.2% | 68.7% |
| Swedish-LinES | Gold tok+mor | - | - | - | - | - | - | - | 86.0% | 82.4% | 78.6% | 79.9% |
| Swedish-Talbanken | Raw text | 99.9% | 96.1% | 95.6% | 93.9% | 94.5% | 92.9% | 95.4% | 82.5% | 78.6% | 70.0% | 70.4% |
| Swedish-Talbanken | Gold tok | - | - | 95.7% | 94.0% | 94.5% | 93.0% | 95.5% | 82.9% | 78.9% | 70.3% | 70.7% |
| Swedish-Talbanken | Gold tok+mor | - | - | - | - | - | - | - | 88.4% | 85.6% | 81.8% | 82.8% |
| Tamil-TTB | Raw text | 94.5% | 97.5% | 81.3% | 76.3% | 80.5% | 75.6% | 84.1% | 58.9% | 52.0% | 42.3% | 43.4% |
| Tamil-TTB | Gold tok | - | - | 85.7% | 80.1% | 84.7% | 79.3% | 88.3% | 65.0% | 56.9% | 46.7% | 47.9% |
| Tamil-TTB | Gold tok+mor | - | - | - | - | - | - | - | 79.0% | 73.1% | 69.0% | 70.0% |
| Telugu-MTG | Raw text | 99.6% | 96.6% | 90.3% | 90.3% | 98.5% | 90.3% | - | 87.1% | 75.8% | 64.8% | 69.6% |
| Telugu-MTG | Gold tok | - | - | 90.6% | 90.6% | 98.9% | 90.6% | - | 88.2% | 76.8% | 65.9% | 70.7% |
| Telugu-MTG | Gold tok+mor | - | - | - | - | - | - | - | 90.3% | 81.5% | 75.6% | 75.8% |
| Turkish-IMST | Raw text | 98.3% | 97.0% | 91.6% | 90.7% | 88.5% | 86.1% | 90.0% | 62.2% | 55.1% | 45.2% | 46.7% |
| Turkish-IMST | Gold tok | - | - | 93.0% | 92.1% | 89.9% | 87.4% | 91.4% | 64.5% | 57.1% | 46.3% | 47.9% |
| Turkish-IMST | Gold tok+mor | - | - | - | - | - | - | - | 66.9% | 61.4% | 56.2% | 57.8% |
| Ukrainian-IU | Raw text | 99.8% | 96.6% | 94.9% | 84.0% | 84.3% | 83.3% | 93.6% | 79.4% | 74.8% | 57.6% | 64.2% |
| Ukrainian-IU | Gold tok | - | - | 95.1% | 84.2% | 84.4% | 83.5% | 93.7% | 79.8% | 75.1% | 57.8% | 64.5% |
| Ukrainian-IU | Gold tok+mor | - | - | - | - | - | - | - | 85.2% | 83.1% | 78.9% | 79.5% |
| Urdu-UDTB | Raw text | 100.0% | 98.3% | 92.4% | 90.1% | 80.8% | 76.1% | 93.1% | 83.6% | 76.9% | 49.5% | 63.5% |
| Urdu-UDTB | Gold tok | - | - | 92.4% | 90.1% | 80.8% | 76.1% | 93.1% | 83.7% | 77.0% | 49.6% | 63.6% |
| Urdu-UDTB | Gold tok+mor | - | - | - | - | - | - | - | 87.5% | 82.6% | 74.8% | 76.3% |
| Uyghur-UDT | Raw text | 99.5% | 81.9% | 87.7% | 89.8% | 84.0% | 76.1% | 91.7% | 70.6% | 56.7% | 37.4% | 44.2% |
| Uyghur-UDT | Gold tok | - | - | 88.2% | 90.3% | 84.4% | 76.6% | 92.2% | 72.0% | 57.9% | 38.0% | 44.9% |
| Uyghur-UDT | Gold tok+mor | - | - | - | - | - | - | - | 74.4% | 61.1% | 50.3% | 52.6% |
| Vietnamese-VTB | Raw text | 85.4% | 93.5% | 76.4% | 74.3% | 85.0% | 74.3% | 84.5% | 45.8% | 40.6% | 34.6% | 36.7% |
| Vietnamese-VTB | Gold tok | - | - | 87.6% | 85.0% | 99.5% | 84.9% | 98.9% | 62.6% | 54.5% | 48.2% | 50.8% |
| Vietnamese-VTB | Gold tok+mor | - | - | - | - | - | - | - | 69.4% | 66.3% | 62.9% | 65.2% |
| Wolof-WTB | Raw text | 99.2% | 92.0% | 91.7% | 91.4% | 91.0% | 88.7% | 93.2% | 77.0% | 70.9% | 58.8% | 60.3% |
| Wolof-WTB | Gold tok | - | - | 92.6% | 92.2% | 91.7% | 89.5% | 93.9% | 78.7% | 72.5% | 60.2% | 61.3% |
| Wolof-WTB | Gold tok+mor | - | - | - | - | - | - | - | 87.1% | 83.7% | 76.6% | 78.1% |
Universal Dependencies 2.4 Models
---------------------------------
Universal Dependencies 2.4 Models are distributed under the CC BY-NC-SA
(http://creativecommons.org/licenses/by-nc-sa/4.0/) licence. The models are
based solely on Universal Dependencies 2.4 (http://hdl.handle.net/11234/1-2988)
treebanks. The models work in UDPipe version 1.2 and later.
Universal Dependencies 2.4 Models are versioned according to the date released
in the format YYMMDD, where YY, MM and DD are two-digit representation of year,
month and day, respectively. The latest version is 190531.
Download
--------
The latest version 190531 of the Universal Dependencies 2.4 models can be
downloaded from LINDAT/CLARIN repository (http://hdl.handle.net/11234/1-2998).
Acknowledgements
----------------
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
LM2015071).
The models were trained on Universal Dependencies 2.4
(http://hdl.handle.net/11234/1-2988) treebanks.
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 W2C
- Web to Corpus (http://hdl.handle.net/11858/00-097C-0000-0022-6133-9).
Publications
------------
- (Straka et al. 2017) Milan Straka and Jana Straková. Tokenizing, POS
Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe
(http://ufal.mff.cuni.cz/~straka/papers/2017-conll_udpipe.pdf). In Proceedings
of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal
Dependencies, Vancouver, Canada, August 2017.
- (Straka et al. 2016) Straka Milan, Hajič Jan, Straková Jana. UDPipe:
Trainable Pipeline for Processing CoNLL-U Files Performing Tokenization,
Morphological Analysis, POS Tagging and Parsing
(http://ufal.mff.cuni.cz/~straka/papers/2016-lrec_udpipe.pdf). In Proceedings
of the Tenth International Conference on Language Resources and Evaluation
(LREC 2016), Portorož, Slovenia, May 2016.
Model Description
-----------------
The Universal Dependencies 2.4 models contain 90 models of 60 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.
The tokenizer is trained using the SpaceAfter=No features. If the features are
not present in the data, they can be filled in using raw text in the language in
question.
The tagger, lemmatizer and parser are trained using gold UD data.
Details about model architecture and training process can be found in the
(Straka et al. 2017) paper.
Reproducible Training
---------------------
In case you want to train the same models, scripts for downloading and
resplitting UD 2.4 data, precomputed word embedding, raw texts for tokenizers,
all hyperparameter values and training scripts are available in the second
archive on the model download page (http://hdl.handle.net/11234/1-2998).
Model Performance
-----------------
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).
|| Treebank | Mode | Words | Sents | UPOS | XPOS | UFeats | AllTags | Lemma | UAS | LAS | MLAS | BLEX |
| Afrikaans-AfriBooms | Raw text | 99.6% | 98.2% | 95.0% | 90.6% | 94.6% | 90.6% | 96.5% | 81.6% | 77.6% | 64.4% | 66.5% |
| Afrikaans-AfriBooms | Gold tok | - | - | 95.3% | 90.8% | 94.9% | 90.8% | 96.7% | 82.5% | 78.4% | 65.1% | 67.2% |
| Afrikaans-AfriBooms | Gold tok+mor | - | - | - | - | - | - | - | 87.6% | 85.0% | 77.0% | 79.6% |
| Ancient Greek-Perseus | Raw text | 100.0% | 98.8% | 82.2% | 72.2% | 85.7% | 72.2% | 82.7% | 64.0% | 57.0% | 30.2% | 38.2% |
| Ancient Greek-Perseus | Gold tok | - | - | 82.2% | 72.2% | 85.7% | 72.2% | 82.7% | 64.1% | 57.2% | 30.3% | 38.4% |
| Ancient Greek-Perseus | Gold tok+mor | - | - | - | - | - | - | - | 68.7% | 63.9% | 53.1% | 57.2% |
| Ancient Greek-PROIEL | Raw text | 100.0% | 51.6% | 96.0% | 96.2% | 88.5% | 87.1% | 93.2% | 72.2% | 67.5% | 50.1% | 56.0% |
| Ancient Greek-PROIEL | Gold tok | - | - | 96.1% | 96.3% | 88.7% | 87.4% | 93.2% | 77.0% | 72.1% | 54.4% | 60.3% |
| Ancient Greek-PROIEL | Gold tok+mor | - | - | - | - | - | - | - | 80.2% | 76.4% | 66.7% | 70.3% |
| Arabic-PADT | Raw text | 94.6% | 82.1% | 90.4% | 84.0% | 84.2% | 83.8% | 88.5% | 72.7% | 68.1% | 56.2% | 59.2% |
| Arabic-PADT | Gold tok | - | - | 95.6% | 89.0% | 89.2% | 88.8% | 92.9% | 82.0% | 76.8% | 63.6% | 66.3% |
| Arabic-PADT | Gold tok+mor | - | - | - | - | - | - | - | 83.9% | 80.7% | 75.7% | 76.8% |
| Armenian-ArmTDP | Raw text | 99.5% | 96.3% | 90.2% | - | 81.4% | 79.9% | 90.3% | 72.1% | 63.1% | 43.6% | 50.6% |
| Armenian-ArmTDP | Gold tok | - | - | 90.6% | - | 81.7% | 80.2% | 90.7% | 72.5% | 63.5% | 43.7% | 50.7% |
| Armenian-ArmTDP | Gold tok+mor | - | - | - | - | - | - | - | 79.8% | 72.9% | 65.0% | 66.7% |
| Basque-BDT | Raw text | 100.0% | 99.8% | 92.3% | - | 87.3% | 84.8% | 93.5% | 75.0% | 69.9% | 57.4% | 63.2% |
| Basque-BDT | Gold tok | - | - | 92.4% | - | 87.4% | 84.8% | 93.6% | 75.1% | 70.0% | 57.4% | 63.2% |
| Basque-BDT | Gold tok+mor | - | - | - | - | - | - | - | 82.1% | 78.4% | 75.2% | 77.2% |
| Belarusian-HSE | Raw text | 99.8% | 73.3% | 83.9% | 31.7% | 66.6% | 24.0% | 75.0% | 58.1% | 52.3% | 28.4% | 33.0% |
| Belarusian-HSE | Gold tok | - | - | 84.1% | 31.9% | 66.8% | 24.1% | 75.2% | 60.1% | 54.1% | 29.3% | 34.1% |
| Belarusian-HSE | Gold tok+mor | - | - | - | - | - | - | - | 72.1% | 69.1% | 62.2% | 63.1% |
| Bulgarian-BTB | Raw text | 99.9% | 93.7% | 97.6% | 94.3% | 95.4% | 93.8% | 94.6% | 89.0% | 85.0% | 75.2% | 73.9% |
| Bulgarian-BTB | Gold tok | - | - | 97.8% | 94.5% | 95.5% | 93.9% | 94.7% | 90.0% | 85.9% | 76.1% | 74.7% |
| Bulgarian-BTB | Gold tok+mor | - | - | - | - | - | - | - | 92.5% | 89.1% | 84.4% | 84.7% |
| Catalan-AnCora | Raw text | 100.0% | 99.6% | 98.1% | 98.0% | 97.7% | 96.9% | 98.2% | 89.0% | 85.9% | 77.4% | 77.8% |
| Catalan-AnCora | Gold tok | - | - | 98.1% | 98.0% | 97.7% | 97.0% | 98.2% | 89.1% | 86.0% | 77.4% | 77.8% |
| Catalan-AnCora | Gold tok+mor | - | - | - | - | - | - | - | 91.0% | 88.5% | 82.5% | 83.0% |
| Chinese-GSD | Raw text | 90.0% | 99.1% | 84.0% | 83.8% | 88.7% | 82.5% | 89.9% | 62.8% | 58.7% | 49.0% | 53.7% |
| Chinese-GSD | Gold tok | - | - | 92.1% | 91.9% | 98.7% | 90.7% | 100.0% | 75.6% | 70.1% | 59.6% | 65.4% |
| Chinese-GSD | Gold tok+mor | - | - | - | - | - | - | - | 84.8% | 81.9% | 75.9% | 78.4% |
| Classical Chinese-Kyoto | Raw text | 99.5% | 38.9% | 90.8% | 87.8% | 91.4% | 85.2% | 99.4% | 66.5% | 60.0% | 56.3% | 58.5% |
| Classical Chinese-Kyoto | Gold tok | - | - | 92.5% | 89.8% | 92.6% | 87.4% | 100.0% | 78.1% | 71.3% | 67.7% | 70.1% |
| Classical Chinese-Kyoto | Gold tok+mor | - | - | - | - | - | - | - | 88.7% | 84.2% | 82.9% | 83.3% |
| Coptic-Scriptorium | Raw text | 64.5% | 29.8% | 60.9% | 60.0% | 61.7% | 59.6% | 62.2% | 35.5% | 33.4% | 21.3% | 24.0% |
| Coptic-Scriptorium | Gold tok | - | - | 93.3% | 91.8% | 95.3% | 91.0% | 94.8% | 81.5% | 75.8% | 59.5% | 63.2% |
| Coptic-Scriptorium | Gold tok+mor | - | - | - | - | - | - | - | 86.5% | 82.0% | 70.0% | 73.1% |
| Croatian-SET | Raw text | 100.0% | 94.0% | 96.9% | 90.1% | 90.7% | 90.1% | 95.3% | 83.8% | 78.1% | 65.1% | 69.8% |
| Croatian-SET | Gold tok | - | - | 97.0% | 90.2% | 90.8% | 90.2% | 95.3% | 84.3% | 78.7% | 65.5% | 70.2% |
| Croatian-SET | Gold tok+mor | - | - | - | - | - | - | - | 87.1% | 82.5% | 77.2% | 79.3% |
| Czech-CAC | Raw text | 100.0% | 99.7% | 98.2% | 90.7% | 89.8% | 89.4% | 97.0% | 87.0% | 83.7% | 70.7% | 77.3% |
| Czech-CAC | Gold tok | - | - | 98.2% | 90.8% | 89.8% | 89.5% | 97.0% | 87.1% | 83.7% | 70.8% | 77.4% |
| Czech-CAC | Gold tok+mor | - | - | - | - | - | - | - | 89.8% | 87.5% | 84.1% | 85.0% |
| Czech-CLTT | Raw text | 99.6% | 98.9% | 97.2% | 87.1% | 87.2% | 86.9% | 96.0% | 79.0% | 75.5% | 60.0% | 69.0% |
| Czech-CLTT | Gold tok | - | - | 97.6% | 87.5% | 87.7% | 87.4% | 96.4% | 79.6% | 76.1% | 60.4% | 69.4% |
| Czech-CLTT | Gold tok+mor | - | - | - | - | - | - | - | 82.7% | 79.6% | 74.7% | 75.3% |
| Czech-FicTree | Raw text | 100.0% | 99.1% | 97.1% | 90.0% | 90.8% | 89.5% | 97.1% | 86.3% | 82.1% | 68.7% | 74.3% |
| Czech-FicTree | Gold tok | - | - | 97.1% | 90.0% | 90.8% | 89.5% | 97.1% | 86.4% | 82.1% | 68.8% | 74.4% |
| Czech-FicTree | Gold tok+mor | - | - | - | - | - | - | - | 90.5% | 87.7% | 83.3% | 84.1% |
| Czech-PDT | Raw text | 99.9% | 93.3% | 98.2% | 92.8% | 92.4% | 91.9% | 97.8% | 86.9% | 83.8% | 74.3% | 79.2% |
| Czech-PDT | Gold tok | - | - | 98.3% | 92.9% | 92.5% | 92.0% | 97.9% | 87.7% | 84.6% | 75.0% | 79.8% |
| Czech-PDT | Gold tok+mor | - | - | - | - | - | - | - | 90.3% | 88.2% | 85.5% | 86.1% |
| Danish-DDT | Raw text | 99.8% | 90.8% | 95.4% | - | 94.8% | 93.4% | 94.7% | 79.2% | 75.8% | 66.3% | 66.6% |
| Danish-DDT | Gold tok | - | - | 95.6% | - | 95.0% | 93.6% | 94.9% | 80.2% | 76.8% | 67.2% | 67.5% |
| Danish-DDT | Gold tok+mor | - | - | - | - | - | - | - | 84.8% | 82.4% | 77.4% | 79.1% |
| Dutch-Alpino | Raw text | 99.8% | 90.0% | 94.2% | 91.5% | 93.5% | 90.8% | 95.3% | 81.8% | 77.6% | 62.5% | 65.5% |
| Dutch-Alpino | Gold tok | - | - | 94.5% | 91.7% | 93.7% | 91.0% | 95.5% | 82.8% | 78.7% | 63.3% | 66.4% |
| Dutch-Alpino | Gold tok+mor | - | - | - | - | - | - | - | 86.3% | 82.8% | 74.1% | 75.8% |
| Dutch-LassySmall | Raw text | 99.8% | 74.0% | 94.2% | 91.9% | 93.8% | 91.2% | 95.6% | 80.3% | 76.2% | 63.6% | 64.5% |
| Dutch-LassySmall | Gold tok | - | - | 94.3% | 92.2% | 94.2% | 91.5% | 95.9% | 83.6% | 79.1% | 67.2% | 68.3% |
| Dutch-LassySmall | Gold tok+mor | - | - | - | - | - | - | - | 87.5% | 84.1% | 78.1% | 78.8% |
| English-EWT | Raw text | 99.0% | 76.3% | 93.5% | 92.9% | 94.4% | 91.5% | 95.5% | 80.0% | 77.0% | 67.6% | 69.6% |
| English-EWT | Gold tok | - | - | 94.5% | 93.9% | 95.4% | 92.5% | 96.3% | 83.9% | 80.7% | 71.2% | 73.2% |
| English-EWT | Gold tok+mor | - | - | - | - | - | - | - | 87.4% | 85.4% | 81.3% | 82.1% |
| English-GUM | Raw text | 99.8% | 84.4% | 93.8% | 93.5% | 94.5% | 92.3% | 94.9% | 80.2% | 76.1% | 64.9% | 64.8% |
| English-GUM | Gold tok | - | - | 94.0% | 93.8% | 94.8% | 92.6% | 95.1% | 81.9% | 77.8% | 66.5% | 66.4% |
| English-GUM | Gold tok+mor | - | - | - | - | - | - | - | 86.5% | 84.3% | 77.9% | 78.7% |
| English-LinES | Raw text | 99.9% | 87.0% | 94.9% | 92.4% | 94.8% | 89.9% | 95.8% | 79.5% | 74.9% | 65.8% | 66.6% |
| English-LinES | Gold tok | - | - | 94.9% | 92.5% | 94.8% | 90.0% | 95.8% | 80.1% | 75.5% | 66.4% | 67.1% |
| English-LinES | Gold tok+mor | - | - | - | - | - | - | - | 84.3% | 80.9% | 76.4% | 78.2% |
| English-ParTUT | Raw text | 99.7% | 100.0% | 93.9% | 93.7% | 93.8% | 92.1% | 96.5% | 84.7% | 81.3% | 69.1% | 71.1% |
| English-ParTUT | Gold tok | - | - | 94.2% | 93.9% | 94.0% | 92.3% | 96.8% | 85.1% | 81.7% | 69.4% | 71.5% |
| English-ParTUT | Gold tok+mor | - | - | - | - | - | - | - | 87.9% | 86.3% | 79.6% | 81.2% |
| Estonian-EDT | Raw text | 100.0% | 91.4% | 95.4% | 96.7% | 93.5% | 91.7% | 90.5% | 79.2% | 75.3% | 67.7% | 64.7% |
| Estonian-EDT | Gold tok | - | - | 95.5% | 96.8% | 93.6% | 91.8% | 90.6% | 80.0% | 76.2% | 68.4% | 65.4% |
| Estonian-EDT | Gold tok+mor | - | - | - | - | - | - | - | 85.0% | 82.7% | 79.4% | 80.5% |
| Estonian-EWT | Raw text | 99.1% | 67.0% | 83.2% | 85.6% | 79.5% | 76.2% | 79.8% | 60.1% | 51.2% | 38.8% | 36.7% |
| Estonian-EWT | Gold tok | - | - | 84.0% | 86.3% | 80.2% | 77.0% | 80.4% | 62.6% | 53.2% | 39.8% | 37.6% |
| Estonian-EWT | Gold tok+mor | - | - | - | - | - | - | - | 74.1% | 69.9% | 64.6% | 65.7% |
| Finnish-FTB | Raw text | 100.0% | 87.4% | 91.5% | 90.8% | 92.6% | 88.9% | 88.5% | 79.5% | 75.0% | 64.2% | 61.0% |
| Finnish-FTB | Gold tok | - | - | 91.8% | 91.0% | 92.7% | 89.2% | 88.6% | 81.1% | 76.6% | 65.9% | 62.6% |
| Finnish-FTB | Gold tok+mor | - | - | - | - | - | - | - | 89.9% | 87.6% | 82.9% | 84.2% |
| Finnish-TDT | Raw text | 99.7% | 88.6% | 94.3% | 95.4% | 92.0% | 90.8% | 86.9% | 80.5% | 76.8% | 68.6% | 62.8% |
| Finnish-TDT | Gold tok | - | - | 94.7% | 95.8% | 92.4% | 91.2% | 87.2% | 81.8% | 78.1% | 69.5% | 63.6% |
| Finnish-TDT | Gold tok+mor | - | - | - | - | - | - | - | 86.8% | 84.7% | 81.6% | 82.5% |
| French-GSD | Raw text | 98.9% | 94.6% | 95.9% | - | 95.6% | 94.5% | 96.7% | 87.5% | 84.5% | 74.0% | 76.1% |
| French-GSD | Gold tok | - | - | 97.0% | - | 96.6% | 95.6% | 97.8% | 89.3% | 86.4% | 75.6% | 77.3% |
| French-GSD | Gold tok+mor | - | - | - | - | - | - | - | 91.2% | 89.2% | 83.3% | 83.7% |
| French-ParTUT | Raw text | 99.4% | 100.0% | 94.6% | 93.8% | 91.9% | 90.5% | 95.2% | 86.0% | 82.2% | 65.2% | 70.0% |
| French-ParTUT | Gold tok | - | - | 95.3% | 94.4% | 92.5% | 91.1% | 95.8% | 86.6% | 82.9% | 66.1% | 70.5% |
| French-ParTUT | Gold tok+mor | - | - | - | - | - | - | - | 89.5% | 87.6% | 79.8% | 80.9% |
| French-Sequoia | Raw text | 99.1% | 87.5% | 96.1% | - | 95.0% | 94.1% | 96.9% | 85.5% | 82.8% | 73.1% | 75.8% |
| French-Sequoia | Gold tok | - | - | 97.1% | - | 95.9% | 95.0% | 97.8% | 87.5% | 84.8% | 75.0% | 77.3% |
| French-Sequoia | Gold tok+mor | - | - | - | - | - | - | - | 90.2% | 88.8% | 84.3% | 84.7% |
| French-Spoken | Raw text | 99.9% | 22.4% | 93.0% | 97.1% | - | 90.6% | 95.6% | 72.6% | 66.8% | 55.1% | 55.9% |
| French-Spoken | Gold tok | - | - | 93.1% | 97.2% | - | 90.8% | 95.7% | 78.3% | 71.7% | 61.8% | 62.1% |
| French-Spoken | Gold tok+mor | - | - | - | - | - | - | - | 82.5% | 78.0% | 69.8% | 71.0% |
| Galician-CTG | Raw text | 99.2% | 97.5% | 96.3% | 95.8% | 99.0% | 95.4% | 96.2% | 79.2% | 76.2% | 62.5% | 65.4% |
| Galician-CTG | Gold tok | - | - | 97.0% | 96.5% | 99.8% | 96.2% | 96.9% | 80.8% | 77.7% | 64.3% | 67.2% |
| Galician-CTG | Gold tok+mor | - | - | - | - | - | - | - | 83.0% | 80.7% | 69.4% | 74.1% |
| Galician-TreeGal | Raw text | 98.8% | 85.4% | 91.1% | 87.3% | 89.5% | 86.6% | 92.5% | 72.1% | 66.6% | 49.8% | 52.3% |
| Galician-TreeGal | Gold tok | - | - | 92.2% | 88.1% | 90.4% | 87.4% | 93.6% | 75.1% | 69.1% | 52.4% | 55.2% |
| Galician-TreeGal | Gold tok+mor | - | - | - | - | - | - | - | 81.7% | 77.5% | 69.4% | 70.7% |
| German-GSD | Raw text | 99.6% | 80.9% | 91.7% | 79.5% | 69.8% | 62.9% | 95.4% | 78.2% | 72.7% | 34.0% | 61.5% |
| German-GSD | Gold tok | - | - | 92.1% | 79.8% | 70.2% | 63.4% | 95.8% | 80.7% | 75.0% | 35.4% | 63.6% |
| German-GSD | Gold tok+mor | - | - | - | - | - | - | - | 85.5% | 81.2% | 72.3% | 75.4% |
| Gothic-PROIEL | Raw text | 100.0% | 31.1% | 94.3% | 94.8% | 87.4% | 85.5% | 92.6% | 68.6% | 62.0% | 48.8% | 54.6% |
| Gothic-PROIEL | Gold tok | - | - | 94.8% | 95.2% | 87.6% | 85.9% | 92.7% | 76.8% | 70.0% | 56.2% | 61.7% |
| Gothic-PROIEL | Gold tok+mor | - | - | - | - | - | - | - | 80.0% | 76.0% | 69.1% | 72.5% |
| Greek-GDT | Raw text | 99.8% | 88.5% | 95.6% | 95.6% | 90.2% | 88.9% | 94.5% | 86.4% | 83.1% | 66.3% | 69.7% |
| Greek-GDT | Gold tok | - | - | 95.9% | 95.9% | 90.5% | 89.2% | 94.7% | 87.0% | 83.7% | 67.1% | 70.5% |
| Greek-GDT | Gold tok+mor | - | - | - | - | - | - | - | 89.5% | 87.6% | 81.6% | 82.5% |
| Hebrew-HTB | Raw text | 85.0% | 99.4% | 80.5% | 80.5% | 78.7% | 77.7% | 81.6% | 61.7% | 58.3% | 44.6% | 47.5% |
| Hebrew-HTB | Gold tok | - | - | 94.9% | 94.9% | 92.7% | 91.5% | 95.4% | 83.6% | 79.6% | 64.3% | 67.1% |
| Hebrew-HTB | Gold tok+mor | - | - | - | - | - | - | - | 87.0% | 84.9% | 78.4% | 78.8% |
| Hindi-HDTB | Raw text | 100.0% | 98.9% | 95.9% | 94.9% | 90.4% | 87.8% | 98.1% | 91.3% | 87.2% | 69.2% | 80.1% |
| Hindi-HDTB | Gold tok | - | - | 95.9% | 95.0% | 90.4% | 87.8% | 98.1% | 91.3% | 87.2% | 69.3% | 80.2% |
| Hindi-HDTB | Gold tok+mor | - | - | - | - | - | - | - | 93.8% | 90.8% | 85.4% | 86.6% |
| Hungarian-Szeged | Raw text | 99.8% | 95.2% | 90.5% | - | 88.0% | 86.4% | 88.5% | 72.7% | 67.1% | 53.6% | 57.8% |
| Hungarian-Szeged | Gold tok | - | - | 90.7% | - | 88.2% | 86.5% | 88.7% | 73.3% | 67.6% | 53.9% | 58.1% |
| Hungarian-Szeged | Gold tok+mor | - | - | - | - | - | - | - | 80.5% | 77.6% | 72.7% | 76.6% |
| Indonesian-GSD | Raw text | 100.0% | 93.9% | 93.0% | 92.2% | 93.9% | 87.1% | 92.2% | 81.0% | 74.5% | 63.6% | 62.9% |
| Indonesian-GSD | Gold tok | - | - | 93.0% | 92.2% | 93.9% | 87.1% | 92.2% | 81.2% | 74.8% | 63.9% | 63.2% |
| Indonesian-GSD | Gold tok+mor | - | - | - | - | - | - | - | 84.0% | 79.8% | 76.4% | 78.3% |
| Irish-IDT | Raw text | 99.6% | 95.9% | 89.6% | 88.5% | 79.5% | 75.8% | 85.6% | 75.2% | 65.3% | 40.2% | 44.3% |
| Irish-IDT | Gold tok | - | - | 90.0% | 88.9% | 79.8% | 76.2% | 86.0% | 75.7% | 65.6% | 40.4% | 44.4% |
| Irish-IDT | Gold tok+mor | - | - | - | - | - | - | - | 79.6% | 72.9% | 61.4% | 63.5% |
| Italian-ISDT | Raw text | 99.8% | 99.4% | 97.1% | 97.0% | 97.0% | 96.1% | 97.3% | 89.2% | 86.7% | 77.3% | 77.5% |
| Italian-ISDT | Gold tok | - | - | 97.4% | 97.2% | 97.2% | 96.4% | 97.5% | 89.5% | 87.0% | 77.7% | 77.8% |
| Italian-ISDT | Gold tok+mor | - | - | - | - | - | - | - | 91.8% | 90.2% | 85.0% | 85.5% |
| Italian-ParTUT | Raw text | 99.8% | 99.0% | 97.0% | 96.5% | 96.4% | 95.2% | 96.5% | 87.5% | 84.4% | 73.2% | 72.4% |
| Italian-ParTUT | Gold tok | - | - | 97.1% | 96.6% | 96.5% | 95.3% | 96.7% | 87.4% | 84.3% | 73.1% | 72.3% |
| Italian-ParTUT | Gold tok+mor | - | - | - | - | - | - | - | 89.7% | 87.6% | 79.8% | 80.7% |
| Italian-PoSTWITA | Raw text | 99.5% | 30.2% | 94.0% | 93.7% | 94.3% | 92.3% | 95.1% | 74.1% | 69.1% | 56.5% | 57.2% |
| Italian-PoSTWITA | Gold tok | - | - | 94.6% | 94.2% | 94.9% | 92.9% | 95.6% | 80.6% | 75.1% | 64.4% | 65.3% |
| Italian-PoSTWITA | Gold tok+mor | - | - | - | - | - | - | - | 84.7% | 80.5% | 74.5% | 75.0% |
| Italian-VIT | Raw text | 99.7% | 94.0% | 96.0% | 94.9% | 95.9% | 93.5% | 96.9% | 84.1% | 80.1% | 68.3% | 69.8% |
| Italian-VIT | Gold tok | - | - | 96.3% | 95.2% | 96.1% | 93.8% | 97.1% | 84.9% | 80.8% | 69.0% | 70.5% |
| Italian-VIT | Gold tok+mor | - | - | - | - | - | - | - | 88.1% | 85.3% | 78.4% | 79.4% |
| Japanese-GSD | Raw text | 91.4% | 94.7% | 88.8% | 88.5% | 91.4% | 88.5% | 90.7% | 76.3% | 74.8% | 62.2% | 64.0% |
| Japanese-GSD | Gold tok | - | - | 96.9% | 96.3% | 100.0% | 96.3% | 99.1% | 92.7% | 90.7% | 81.2% | 83.3% |
| Japanese-GSD | Gold tok+mor | - | - | - | - | - | - | - | 95.3% | 94.3% | 88.0% | 88.1% |
| Korean-GSD | Raw text | 99.8% | 94.0% | 93.5% | 81.7% | 99.5% | 79.5% | 87.0% | 69.5% | 61.4% | 54.1% | 50.8% |
| Korean-GSD | Gold tok | - | - | 93.7% | 81.9% | 99.7% | 79.7% | 87.2% | 70.3% | 62.1% | 54.8% | 51.4% |
| Korean-GSD | Gold tok+mor | - | - | - | - | - | - | - | 72.5% | 65.5% | 60.4% | 61.6% |
| Korean-Kaist | Raw text | 100.0% | 100.0% | 93.3% | 80.1% | - | 80.1% | 88.5% | 77.9% | 70.6% | 61.9% | 58.1% |
| Korean-Kaist | Gold tok | - | - | 93.4% | 80.1% | - | 80.1% | 88.5% | 78.0% | 70.7% | 62.0% | 58.1% |
| Korean-Kaist | Gold tok+mor | - | - | - | - | - | - | - | 80.3% | 73.7% | 67.8% | 68.4% |
| Latin-ITTB | Raw text | 100.0% | 90.7% | 97.1% | 93.0% | 93.3% | 91.4% | 98.0% | 83.2% | 79.9% | 70.5% | 75.3% |
| Latin-ITTB | Gold tok | - | - | 97.1% | 93.0% | 93.3% | 91.4% | 98.0% | 83.9% | 80.5% | 70.7% | 75.5% |
| Latin-ITTB | Gold tok+mor | - | - | - | - | - | - | - | 87.8% | 85.8% | 81.9% | 83.0% |
| Latin-PROIEL | Raw text | 99.9% | 36.8% | 94.5% | 94.7% | 86.7% | 85.6% | 94.5% | 65.9% | 60.1% | 47.7% | 54.4% |
| Latin-PROIEL | Gold tok | - | - | 94.7% | 94.8% | 87.2% | 86.2% | 94.7% | 73.4% | 67.3% | 54.9% | 61.3% |
| Latin-PROIEL | Gold tok+mor | - | - | - | - | - | - | - | 77.2% | 73.4% | 67.1% | 70.3% |
| Latin-Perseus | Raw text | 100.0% | 98.5% | 83.3% | 67.2% | 72.1% | 67.2% | 78.0% | 57.7% | 47.1% | 29.4% | 31.9% |
| Latin-Perseus | Gold tok | - | - | 83.3% | 67.2% | 72.1% | 67.2% | 77.9% | 57.9% | 47.2% | 29.4% | 31.9% |
| Latin-Perseus | Gold tok+mor | - | - | - | - | - | - | - | 67.6% | 61.8% | 55.9% | 59.1% |
| Latvian-LVTB | Raw text | 99.4% | 99.0% | 93.5% | 84.0% | 89.2% | 83.6% | 92.7% | 79.3% | 74.3% | 61.4% | 65.5% |
| Latvian-LVTB | Gold tok | - | - | 94.0% | 84.4% | 89.7% | 84.1% | 93.2% | 80.0% | 74.9% | 62.1% | 66.1% |
| Latvian-LVTB | Gold tok+mor | - | - | - | - | - | - | - | 86.3% | 83.0% | 79.1% | 80.3% |
| Lithuanian-ALKSNIS | Raw text | 99.3% | 87.0% | 87.4% | 76.2% | 77.9% | 75.7% | 85.3% | 63.6% | 56.2% | 40.8% | 44.7% |
| Lithuanian-ALKSNIS | Gold tok | - | - | 87.9% | 76.7% | 78.3% | 76.1% | 85.8% | 64.9% | 57.5% | 41.5% | 45.5% |
| Lithuanian-ALKSNIS | Gold tok+mor | - | - | - | - | - | - | - | 72.7% | 69.2% | 65.1% | 66.5% |
| Lithuanian-HSE | Raw text | 98.1% | 92.0% | 73.6% | 72.0% | 68.4% | 61.5% | 72.3% | 46.0% | 34.0% | 20.8% | 23.1% |
| Lithuanian-HSE | Gold tok | - | - | 74.2% | 72.5% | 69.2% | 62.0% | 73.1% | 47.1% | 34.6% | 20.9% | 23.2% |
| Lithuanian-HSE | Gold tok+mor | - | - | - | - | - | - | - | 54.9% | 47.7% | 41.9% | 44.2% |
| Maltese-MUDT | Raw text | 99.8% | 85.2% | 93.8% | 93.5% | - | 93.2% | - | 77.3% | 71.5% | 58.2% | 61.8% |
| Maltese-MUDT | Gold tok | - | - | 93.9% | 93.7% | - | 93.4% | - | 77.9% | 72.0% | 58.6% | 62.2% |
| Maltese-MUDT | Gold tok+mor | - | - | - | - | - | - | - | 82.1% | 77.7% | 68.0% | 69.5% |
| Marathi-UFAL | Raw text | 91.0% | 84.0% | 72.3% | - | 61.6% | 59.1% | 76.8% | 59.4% | 48.9% | 23.0% | 30.1% |
| Marathi-UFAL | Gold tok | - | - | 77.7% | - | 63.4% | 60.4% | 76.5% | 68.5% | 54.9% | 26.0% | 31.0% |
| Marathi-UFAL | Gold tok+mor | - | - | - | - | - | - | - | 77.9% | 67.7% | 60.7% | 63.1% |
| North Sami-Giella | Raw text | 99.9% | 98.8% | 87.8% | 89.4% | 82.5% | 78.4% | 82.0% | 64.7% | 57.9% | 46.7% | 43.2% |
| North Sami-Giella | Gold tok | - | - | 87.9% | 89.6% | 82.6% | 78.5% | 82.1% | 64.9% | 58.2% | 46.9% | 43.5% |
| North Sami-Giella | Gold tok+mor | - | - | - | - | - | - | - | 81.4% | 78.7% | 74.2% | 77.2% |
| Norwegian-Bokmaal | Raw text | 99.8% | 96.9% | 96.5% | - | 95.2% | 94.0% | 96.7% | 87.2% | 84.3% | 75.2% | 77.2% |
| Norwegian-Bokmaal | Gold tok | - | - | 96.7% | - | 95.4% | 94.2% | 97.0% | 87.8% | 84.9% | 75.7% | 77.7% |
| Norwegian-Bokmaal | Gold tok+mor | - | - | - | - | - | - | - | 91.7% | 89.9% | 85.8% | 86.6% |
| Norwegian-Nynorsk | Raw text | 99.9% | 93.4% | 96.1% | - | 94.9% | 93.6% | 96.3% | 85.8% | 82.8% | 72.9% | 74.5% |
| Norwegian-Nynorsk | Gold tok | - | - | 96.2% | - | 95.0% | 93.7% | 96.4% | 86.4% | 83.5% | 73.6% | 75.2% |
| Norwegian-Nynorsk | Gold tok+mor | - | - | - | - | - | - | - | 91.1% | 89.2% | 84.7% | 85.8% |
| Norwegian-NynorskLIA | Raw text | 99.8% | 99.5% | 93.7% | - | 93.2% | 90.4% | 96.2% | 73.4% | 68.3% | 55.8% | 59.9% |
| Norwegian-NynorskLIA | Gold tok | - | - | 93.8% | - | 93.3% | 90.5% | 96.4% | 73.7% | 68.6% | 56.0% | 60.0% |
| Norwegian-NynorskLIA | Gold tok+mor | - | - | - | - | - | - | - | 80.7% | 76.3% | 68.7% | 70.9% |
| Old Church Slavonic-PROIEL | Raw text | 100.0% | 41.0% | 93.6% | 93.7% | 86.8% | 85.6% | 90.9% | 71.9% | 65.9% | 55.3% | 60.0% |
| Old Church Slavonic-PROIEL | Gold tok | - | - | 93.8% | 94.0% | 87.4% | 86.2% | 91.0% | 79.5% | 73.2% | 62.4% | 66.0% |
| Old Church Slavonic-PROIEL | Gold tok+mor | - | - | - | - | - | - | - | 85.1% | 81.3% | 76.8% | 79.6% |
| Old French-SRCMF | Raw text | 99.9% | 100.0% | 94.2% | 93.8% | 96.0% | 93.3% | - | 85.5% | 79.3% | 70.9% | 74.6% |
| Old French-SRCMF | Gold tok | - | - | 94.3% | 93.9% | 96.1% | 93.4% | - | 85.6% | 79.4% | 71.0% | 74.7% |
| Old French-SRCMF | Gold tok+mor | - | - | - | - | - | - | - | 88.8% | 84.4% | 78.4% | 79.7% |
| Old Russian-TOROT | Raw text | 100.0% | 29.6% | 89.8% | 89.8% | 82.1% | 79.9% | 81.1% | 63.4% | 56.7% | 43.3% | 44.0% |
| Old Russian-TOROT | Gold tok | - | - | 90.4% | 90.5% | 82.8% | 80.8% | 81.1% | 73.3% | 66.1% | 51.9% | 50.8% |
| Old Russian-TOROT | Gold tok+mor | - | - | - | - | - | - | - | 80.5% | 76.4% | 70.4% | 73.1% |
| Persian-Seraji | Raw text | 99.7% | 98.8% | 96.0% | 95.9% | 96.1% | 95.4% | 93.6% | 83.6% | 79.6% | 72.8% | 70.1% |
| Persian-Seraji | Gold tok | - | - | 96.3% | 96.3% | 96.4% | 95.7% | 93.9% | 84.3% | 80.2% | 73.3% | 70.5% |
| Persian-Seraji | Gold tok+mor | - | - | - | - | - | - | - | 87.2% | 84.3% | 80.0% | 80.8% |
| Polish-LFG | Raw text | 99.8% | 99.7% | 96.7% | 87.2% | 89.1% | 86.5% | 94.5% | 90.9% | 87.4% | 74.4% | 78.5% |
| Polish-LFG | Gold tok | - | - | 96.9% | 87.3% | 89.2% | 86.7% | 94.6% | 91.3% | 87.8% | 74.7% | 78.8% |
| Polish-LFG | Gold tok+mor | - | - | - | - | - | - | - | 96.2% | 94.8% | 92.9% | 93.1% |
| Polish-PDB | Raw text | 99.9% | 97.0% | 97.2% | 88.3% | 88.8% | 87.8% | 95.8% | 87.6% | 83.5% | 69.2% | 76.0% |
| Polish-PDB | Gold tok | - | - | 97.3% | 88.4% | 88.9% | 87.9% | 96.0% | 88.1% | 84.0% | 69.6% | 76.4% |
| Polish-PDB | Gold tok+mor | - | - | - | - | - | - | - | 90.9% | 88.8% | 85.5% | 86.0% |
| Portuguese-Bosque | Raw text | 99.5% | 90.2% | 95.7% | - | 94.4% | 92.3% | 96.6% | 86.5% | 82.6% | 67.4% | 72.4% |
| Portuguese-Bosque | Gold tok | - | - | 96.2% | - | 94.8% | 92.8% | 97.1% | 87.7% | 83.8% | 68.3% | 73.6% |
| Portuguese-Bosque | Gold tok+mor | - | - | - | - | - | - | - | 89.3% | 86.2% | 79.3% | 80.2% |
| Portuguese-GSD | Raw text | 99.9% | 96.5% | 97.0% | 97.0% | 99.8% | 97.0% | 98.6% | 88.0% | 85.9% | 77.9% | 78.5% |
| Portuguese-GSD | Gold tok | - | - | 97.2% | 97.2% | 99.9% | 97.2% | 98.7% | 88.4% | 86.3% | 78.2% | 78.8% |
| Portuguese-GSD | Gold tok+mor | - | - | - | - | - | - | - | 90.9% | 89.5% | 83.7% | 84.3% |
| Romanian-Nonstandard | Raw text | 98.3% | 96.7% | 93.8% | 88.9% | 87.8% | 86.5% | 91.8% | 81.7% | 76.0% | 57.5% | 64.0% |
| Romanian-Nonstandard | Gold tok | - | - | 95.5% | 90.5% | 89.3% | 88.0% | 93.3% | 84.0% | 78.2% | 59.2% | 65.6% |
| Romanian-Nonstandard | Gold tok+mor | - | - | - | - | - | - | - | 87.1% | 81.7% | 73.5% | 75.5% |
| Romanian-RRT | Raw text | 99.7% | 95.3% | 96.7% | 95.9% | 96.1% | 95.7% | 96.6% | 85.3% | 80.0% | 71.5% | 71.6% |
| Romanian-RRT | Gold tok | - | - | 96.9% | 96.2% | 96.4% | 96.0% | 96.8% | 86.0% | 80.6% | 72.0% | 72.1% |
| Romanian-RRT | Gold tok+mor | - | - | - | - | - | - | - | 87.9% | 83.1% | 76.9% | 77.9% |
| Russian-GSD | Raw text | 99.5% | 96.2% | 95.0% | 94.7% | 85.4% | 84.3% | 92.3% | 82.4% | 77.2% | 61.8% | 66.8% |
| Russian-GSD | Gold tok | - | - | 95.4% | 95.1% | 85.8% | 84.7% | 92.7% | 83.3% | 78.1% | 62.4% | 67.6% |
| Russian-GSD | Gold tok+mor | - | - | - | - | - | - | - | 86.7% | 83.5% | 80.5% | 81.1% |
| Russian-SynTagRus | Raw text | 99.6% | 98.8% | 97.8% | - | 93.5% | 93.2% | 96.5% | 87.6% | 85.0% | 77.0% | 79.4% |
| Russian-SynTagRus | Gold tok | - | - | 98.2% | - | 93.9% | 93.5% | 96.9% | 88.3% | 85.7% | 77.5% | 79.9% |
| Russian-SynTagRus | Gold tok+mor | - | - | - | - | - | - | - | 90.3% | 89.0% | 86.9% | 87.3% |
| Russian-Taiga | Raw text | 97.6% | 76.0% | 88.3% | 91.5% | 77.0% | 71.0% | 84.9% | 65.5% | 58.2% | 38.5% | 44.0% |
| Russian-Taiga | Gold tok | - | - | 90.4% | 93.8% | 79.2% | 72.9% | 87.0% | 69.9% | 62.1% | 41.4% | 47.5% |
| Russian-Taiga | Gold tok+mor | - | - | - | - | - | - | - | 76.0% | 71.7% | 66.2% | 67.7% |
| Serbian-SET | Raw text | 100.0% | 93.0% | 97.4% | 91.1% | 91.5% | 91.1% | 95.1% | 86.5% | 82.4% | 70.0% | 74.0% |
| Serbian-SET | Gold tok | - | - | 97.4% | 91.2% | 91.6% | 91.2% | 95.1% | 87.1% | 83.1% | 70.7% | 74.7% |
| Serbian-SET | Gold tok+mor | - | - | - | - | - | - | - | 89.7% | 86.5% | 82.3% | 83.7% |
| Slovak-SNK | Raw text | 100.0% | 85.3% | 92.9% | 77.0% | 80.3% | 76.7% | 86.6% | 81.0% | 76.3% | 56.1% | 60.7% |
| Slovak-SNK | Gold tok | - | - | 93.0% | 77.2% | 80.5% | 76.8% | 86.6% | 82.5% | 77.8% | 57.2% | 61.5% |
| Slovak-SNK | Gold tok+mor | - | - | - | - | - | - | - | 88.9% | 86.7% | 83.7% | 84.5% |
| Slovenian-SSJ | Raw text | 100.0% | 98.1% | 96.1% | 88.3% | 88.7% | 87.7% | 95.3% | 84.9% | 81.5% | 66.9% | 72.5% |
| Slovenian-SSJ | Gold tok | - | - | 96.2% | 88.3% | 88.7% | 87.8% | 95.3% | 85.0% | 81.6% | 67.0% | 72.6% |
| Slovenian-SSJ | Gold tok+mor | - | - | - | - | - | - | - | 92.0% | 90.4% | 87.2% | 87.6% |
| Slovenian-SST | Raw text | 99.8% | 23.1% | 88.4% | 80.2% | 80.3% | 77.7% | 91.0% | 53.9% | 47.0% | 34.4% | 38.5% |
| Slovenian-SST | Gold tok | - | - | 88.8% | 80.7% | 80.9% | 78.3% | 91.2% | 64.7% | 56.9% | 43.1% | 48.6% |
| Slovenian-SST | Gold tok+mor | - | - | - | - | - | - | - | 74.8% | 69.4% | 63.7% | 65.6% |
| Spanish-AnCora | Raw text | 100.0% | 98.3% | 98.3% | 98.1% | 98.1% | 97.4% | 98.5% | 88.2% | 85.1% | 77.0% | 77.5% |
| Spanish-AnCora | Gold tok | - | - | 98.4% | 98.2% | 98.2% | 97.4% | 98.5% | 88.4% | 85.3% | 77.2% | 77.7% |
| Spanish-AnCora | Gold tok+mor | - | - | - | - | - | - | - | 90.2% | 87.6% | 81.4% | 82.3% |
| Spanish-GSD | Raw text | 99.8% | 94.9% | 95.4% | - | 96.2% | 93.7% | 95.9% | 85.3% | 81.9% | 68.7% | 69.3% |
| Spanish-GSD | Gold tok | - | - | 95.7% | - | 96.5% | 93.9% | 96.1% | 85.9% | 82.5% | 69.2% | 69.8% |
| Spanish-GSD | Gold tok+mor | - | - | - | - | - | - | - | 88.3% | 85.6% | 78.6% | 79.5% |
| Swedish-LinES | Raw text | 100.0% | 86.8% | 94.4% | 91.6% | 87.6% | 84.4% | 94.5% | 80.1% | 75.2% | 59.6% | 67.2% |
| Swedish-LinES | Gold tok | - | - | 94.5% | 91.7% | 87.7% | 84.5% | 94.5% | 80.8% | 75.8% | 60.0% | 67.6% |
| Swedish-LinES | Gold tok+mor | - | - | - | - | - | - | - | 85.7% | 81.8% | 78.1% | 79.6% |
| Swedish-Talbanken | Raw text | 99.9% | 96.1% | 95.6% | 93.9% | 94.5% | 92.9% | 95.4% | 82.5% | 78.6% | 70.0% | 70.4% |
| Swedish-Talbanken | Gold tok | - | - | 95.7% | 94.0% | 94.5% | 93.0% | 95.5% | 82.9% | 78.9% | 70.3% | 70.7% |
| Swedish-Talbanken | Gold tok+mor | - | - | - | - | - | - | - | 88.4% | 85.6% | 81.8% | 82.8% |
| Tamil-TTB | Raw text | 94.5% | 97.5% | 81.3% | 76.3% | 80.5% | 75.6% | 84.1% | 58.9% | 52.0% | 42.3% | 43.4% |
| Tamil-TTB | Gold tok | - | - | 85.7% | 80.1% | 84.7% | 79.3% | 88.3% | 65.0% | 56.9% | 46.7% | 47.9% |
| Tamil-TTB | Gold tok+mor | - | - | - | - | - | - | - | 79.0% | 73.1% | 69.0% | 70.0% |
| Telugu-MTG | Raw text | 99.8% | 96.6% | 90.5% | 90.5% | 98.7% | 90.5% | - | 87.3% | 75.7% | 64.8% | 69.4% |
| Telugu-MTG | Gold tok | - | - | 90.6% | 90.6% | 98.9% | 90.6% | - | 88.2% | 76.8% | 65.9% | 70.7% |
| Telugu-MTG | Gold tok+mor | - | - | - | - | - | - | - | 90.3% | 81.5% | 75.6% | 75.8% |
| Turkish-IMST | Raw text | 98.3% | 97.0% | 91.6% | 90.7% | 88.5% | 86.1% | 90.0% | 62.2% | 55.1% | 45.2% | 46.7% |
| Turkish-IMST | Gold tok | - | - | 93.0% | 92.1% | 89.9% | 87.4% | 91.4% | 64.5% | 57.1% | 46.3% | 47.9% |
| Turkish-IMST | Gold tok+mor | - | - | - | - | - | - | - | 66.9% | 61.4% | 56.2% | 57.8% |
| Ukrainian-IU | Raw text | 99.8% | 96.6% | 94.9% | 84.0% | 84.3% | 83.3% | 93.6% | 79.4% | 74.8% | 57.6% | 64.2% |
| Ukrainian-IU | Gold tok | - | - | 95.1% | 84.2% | 84.4% | 83.5% | 93.7% | 79.8% | 75.1% | 57.8% | 64.5% |
| Ukrainian-IU | Gold tok+mor | - | - | - | - | - | - | - | 85.2% | 83.1% | 78.9% | 79.5% |
| Urdu-UDTB | Raw text | 100.0% | 98.6% | 92.4% | 90.1% | 80.8% | 76.1% | 93.1% | 83.6% | 76.9% | 49.5% | 63.5% |
| Urdu-UDTB | Gold tok | - | - | 92.4% | 90.1% | 80.8% | 76.1% | 93.1% | 83.7% | 77.0% | 49.6% | 63.6% |
| Urdu-UDTB | Gold tok+mor | - | - | - | - | - | - | - | 87.5% | 82.6% | 74.8% | 76.3% |
| Uyghur-UDT | Raw text | 99.7% | 82.9% | 87.9% | 90.0% | 84.1% | 76.3% | 91.9% | 70.8% | 56.7% | 37.4% | 44.1% |
| Uyghur-UDT | Gold tok | - | - | 88.2% | 90.3% | 84.4% | 76.6% | 92.2% | 72.0% | 57.9% | 38.0% | 44.9% |
| Uyghur-UDT | Gold tok+mor | - | - | - | - | - | - | - | 74.4% | 61.1% | 50.3% | 52.6% |
| Vietnamese-VTB | Raw text | 85.4% | 93.0% | 76.4% | 74.4% | 85.1% | 74.3% | 84.6% | 46.4% | 41.2% | 35.1% | 37.1% |
| Vietnamese-VTB | Gold tok | - | - | 87.6% | 85.0% | 99.5% | 84.9% | 98.9% | 62.6% | 54.5% | 48.2% | 50.8% |
| Vietnamese-VTB | Gold tok+mor | - | - | - | - | - | - | - | 69.4% | 66.3% | 62.9% | 65.2% |
| Wolof-WTB | Raw text | 99.2% | 92.0% | 91.7% | 91.4% | 91.0% | 88.7% | 93.2% | 77.0% | 70.9% | 58.8% | 60.3% |
| Wolof-WTB | Gold tok | - | - | 92.6% | 92.2% | 91.7% | 89.5% | 93.9% | 78.7% | 72.5% | 60.2% | 61.3% |
| Wolof-WTB | Gold tok+mor | - | - | - | - | - | - | - | 87.1% | 83.7% | 76.6% | 78.1% |
Universal Dependencies 2.3 Models
---------------------------------
Universal Dependencies 2.3 Models are distributed under the CC BY-NC-SA
(http://creativecommons.org/licenses/by-nc-sa/4.0/) licence. The models are
based solely on Universal Dependencies 2.3 (http://hdl.handle.net/11234/1-2895)
treebanks. The models work in UDPipe version 1.2 and later.
Universal Dependencies 2.3 Models are versioned according to the date released
in the format YYMMDD, where YY, MM and DD are two-digit representation of year,
month and day, respectively. The latest version is 181115.
Download
--------
The latest version 181115 of the Universal Dependencies 2.3 models can be
downloaded from LINDAT/CLARIN repository (http://hdl.handle.net/11234/1-2898).
Acknowledgements
----------------