Search results

• EVALD 3.0 – Evaluator of Discourse

  3 resources

  EVALD 3.0 serves for automatic evaluation of surface coherence (cohesion) in Czech texts written by native speakers of Czech.

  Use "EVALD 3.0 – Evaluator of Discourse"

• Universal Dependencies 2.10 models for UDPipe 2 (2022-07-11)

  2 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for 123 treebanks of 69 languages of Universal Depenencies 2.10 Treebanks, created solely using UD 2.10 data (https://hdl.handle.net/11234/1-4758). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_210_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

  Use "Universal Dependencies 2.10 models for UDPipe 2 (2022-07-11)"

• EVALD 4.0 – Evaluator of Discourse

  3 resources

  EVALD 4.0 serves for automatic evaluation of surface coherence (cohesion) in Czech texts written by native speakers of Czech.

  Use "EVALD 4.0 – Evaluator of Discourse"

• Czech PDT-C 1.0 Model for UDPipe 2 (2023-11-16)

  2 resources

  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 .

  Use "Czech PDT-C 1.0 Model for UDPipe 2 (2023-11-16)"

• Translation Models (en-ru) (v1.0)

  2 resources

  En-Ru 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->ru: 18.0 ru->en: 30.4 (Evaluated using multeval: https://github.com/jhclark/multeval)

  Use "Translation Models (en-ru) (v1.0)"

• CUBBITT Translation Models (en-pl) (v1.0)

  3 resources

  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)

  Use "CUBBITT Translation Models (en-pl) (v1.0)"

• Universal Dependencies 1.2 Models for Parsito

  2 resources

  Parsing models for all Universal Depenencies 1.2 Treebanks, created solely using UD 1.2 data (http://hdl.handle.net/11234/1-1548). To use these models, you need Parsito binary, which you can download from http://hdl.handle.net/11234/1-1584.

  Use "Universal Dependencies 1.2 Models for Parsito"

• The Model latinpipe-evalatin24-240520 for LatinPipe 2024

  2 resources

  The latinpipe-evalatin24-240520 is a PhilBerta-based model for LatinPipe 2024 <https://github.com/ufal/evalatin2024-latinpipe>, performing tagging, lemmatization, and dependency parsing of Latin, based on the winning entry to the EvaLatin 2024 <https://circse.github.io/LT4HALA/2024/EvaLatin> shared task. It is released under the CC BY-NC-SA 4.0 license.

  Use "The Model latinpipe-evalatin24-240520 for LatinPipe 2024"

• WMT21 Marian translation model (ca-oc multi-task)

  1 resources

  Marian NMT model for Catalan to Occitan translation. It is a multi-task model, producing also a phonemic transcription of the Catalan source. The model was submitted to WMT'21 Shared Task on Multilingual Low-Resource Translation for Indo-European Languages as a CUNI-Contrastive system for Catalan to Occitan.

  Use "WMT21 Marian translation model (ca-oc multi-task)"

• CorPipe 23 multilingual CorefUD 1.1 model (corpipe23-corefud1.1-231206)

  2 resources

  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).

  Use "CorPipe 23 multilingual CorefUD 1.1 model (corpipe23-corefud1.1-231206)"