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)
Web corpus of Czech, created in 2011. Contains newspapers+magazines, discussions, blogs. See http://www.lrec-conf.org/proceedings/lrec2012/summaries/120.html for details. and GA405/09/0278
A co-occurrence database, developed by the Institut fuer Deutsche Sprache, for research in the field of collocation analysis in modern German. The database holds over 200,000 analysed words that can be browsed or searched and shown in context.
This is a document-aligned parallel corpus of English and Czech abstracts of scientific papers published by authors from the Institute of Formal and Applied Linguistics, Charles University in Prague, as reported in the institute's system Biblio. For each publication, the authors are obliged to provide both the original abstract in Czech or English, and its translation into English or Czech, respectively. No filtering was performed, except for removing entries missing the Czech or English abstract, and replacing newline and tabulator characters by spaces.
This is a parallel corpus of Czech and mostly English abstracts of scientific papers and presentations published by authors from the Institute of Formal and Applied Linguistics, Charles University in Prague. For each publication record, the authors are obliged to provide both the original abstract (in Czech or English), and its translation (English or Czech) in the internal Biblio system. The data was filtered for duplicates and missing entries, ensuring that every record is bilingual. Additionally, records of published papers which are indexed by SemanticScholar contain the respective link. The dataset was created from September 2022 image of the Biblio database and is stored in JSONL format, with each line corresponding to one record.
The database contains annotated reflective sentences, which fall into the categories of reflective writing according to Ullmann's (2019) model. The dataset is ready to replicate these categories' prediction using machine learning. Available from: https://anonymous.4open.science/repository/c856595c-dfc2-48d7-aa3d-0ccc2648c4dc/data
This is the Czech Court Decisions Corpus (CzCDC 1.0). This corpus contains whole texts of the decisions from three top-tier courts (Supreme, Supreme Administrative and Constitutional court) in Czech republic. Court decisions are published from 1st January 1993 to 30th September 2018.
The language of decisions is Czech. Content of decisions is unedited and obtained directly from the competent court.
Decisions are in .txt format in three folders divided by courts.
Corpus contains three .csv files containing the list of all decisions with four columns:
- name of the file: exact file name of a decision with extension .txt;
- decision identifier (docket number): official identification of the decision as issued by the court;
- date of decision: in ISO 8601 (YYYY-MM-DD);
- court abbreviation: SupCo for Supreme Court, SupAdmCo for Supreme Administrative Court, ConCo for Constitutional Court
Statistics:
- SupCo: 111 977 decisions, 23 699 639 lines, 224 061 129 words, 1 462 948 200 bits;
- SupAdmCo: 52 660 decisions, 18 069 993 lines, 137 839 985 words, 1 067 826 507 bits;
- ConCo: 73 086 decisions, 6 178 371 lines, 98 623 753 words, 664 657 755 bits
- all courts combined: 237 723 decisions, 47 948 003 lines, 460 524 867 words, 3 195 432 462 bits
AGREE is a dataset and task for evaluation of language models based on grammar agreement in Czech. The dataset consists of sentences with marked suffixes of past tense verbs. The task is to choose the right verb suffix which depends on gender, number and animacy of subject. It is challenging for language models because 1) Czech is morphologically rich, 2) it has relatively free word order, 3) high out-of-vocabulary (OOV) ratio, 4) predicate and subject can be far from each other, 5) subjects can be unexpressed and 6) various semantic rules may apply. The task provides a straightforward and easily reproducible way of evaluating language models on a morphologically rich language.