Chared is a software tool which can detect character encoding of a text document provided the language of the document is known. The language of the text has to be specified as an input parameter so that the corresponding language model can be used. The package contains models for a wide range of languages (currently 57 --- covering all major languages). Furthermore, it provides a training script to learn models for additional languages using a set of user supplied sample html pages in the given language. The detection algorithm is based on determining similarity of byte trigrams vectors. In general, chared should be more accurate than other character encoding detection tools with no language constraints. This is an important advantage allowing precise character decoding needed for building large textual corpora. The tool has been used for building corpora in American Spanish, Arabic, Czech, French, Japanese, Russian, Tajik, and six Turkic languages consisting of 70 billions tokens altogether. Chared is an open source software, licensed under New BSD License and available for download (including the source code) at http://code.google.com/p/chared/. The research leading to this piece of software was published in POMIKÁLEK, Jan a Vít SUCHOMEL. chared: Character Encoding Detection with a Known Language. In Aleš Horák, Pavel Rychlý. RASLAN 2011. 5. vyd. Brno, Czech Republic: Tribun EU, 2011. od s. 125-129, 5 s. ISBN 978-80-263-0077-9. and PRESEMT, Lexical Computing Ltd
The `corpipe23-corefud1.1-231206` is a `mT5-large`-based multilingual model for coreference resolution usable in CorPipe 23 (https://github.com/ufal/crac2023-corpipe). It is released under the CC BY-NC-SA 4.0 license.
The model is language agnostic (no _corpus id_ on input), so it can be used to predict coreference in any `mT5` language (for zero-shot evaluation, see the paper). However, note that the empty nodes must be present already on input, they are not predicted (the same settings as in the CRAC23 shared task).
CUBBITT En-Cs translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on newstest2014 (BLEU):
en->cs: 27.6
cs->en: 34.4
(Evaluated using multeval: https://github.com/jhclark/multeval)
CUBBITT En-Fr translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on newstest2014 (BLEU):
en->fr: 38.2
fr->en: 36.7
(Evaluated using multeval: https://github.com/jhclark/multeval)
CUBBITT En-Pl translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on newstest2020 (BLEU):
en->pl: 12.3
pl->en: 20.0
(Evaluated using multeval: https://github.com/jhclark/multeval)
Tokenizer, POS Tagger, Lemmatizer, and Parser model based on the PDT-C 1.0 treebank (https://hdl.handle.net/11234/1-3185). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#czech_pdtc1.0_model . To use these models, you need UDPipe version 2.1, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .
DZ Interset is a means of converting among various tag sets in natural language processing. The core idea is similar to interlingua-based machine translation. DZ Interset defines a set of features that are encoded by the various tag sets. The set of features should be as universal as possible. It does not need to encode everything that is encoded by any tag set but it should encode all information that people may want to access and/or port from one tag set to another.
New tag sets are attached by writing a driver for them. Once the driver is ready, you can easily convert tags between the new set and any other set for which you also have a driver. This reusability is an obvious advantage over writing a targeted conversion procedure each time you need to convert between a particular pair of tag sets. and grant MSM 0021620838 of the Ministry of Education of the Czech Republic
Extremely fast digital audio channelizer implementation, usable as a building block for experimental ASR front-ends or signal denoising applications. Also applicable in software defined radios, due to its high throughput. It comes in a form of a C/C++ library and an executable example program which reads input stream, splitting it into equidistant frequency channels, emitting their data to the output.
Features:
(1) Hand tuned SIMD-aware assembly for x86 (SSE) and IA64 (AVX) as well as for ARM (NEON) processors.
(2) Generic non-SIMD C++ implementation for other architectures.
(3) Capable of taking advantage of multicore CPUs.
(4) Fully configurable number of channels and the output decimation rate.
(5) User supplied FIR of the channel separation filter, which allows to specify the width of the channels, whether they should overlap or be separated.
(6) Input and output signal samples are treated as complex numbers.
(7) Speed over 750 complex MS/s achieved on Core i7 4710HQ @ 2.5GHz, when channelizing into 72 output channels with a FIR length of 1152 samples, using 3 computing threads.
(8) Runs under Linux OS.
POS Tagger and Lemmatizer models for EvaLatin2020 data (https://github.com/CIRCSE/LT4HALA). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#evalatin20_models .
To use these models, you need UDPipe version at least 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .