Pretrained model weights for the UDify model, and extracted BERT weights in pytorch-transformers format. Note that these weights slightly differ from those used in the paper.
UDPipe is an trainable pipeline for tokenization, tagging, lemmatization and dependency parsing of CoNLL-U files. UDPipe is language-agnostic and can be trained given only annotated data in CoNLL-U format. Trained models are provided for nearly all UD treebanks. UDPipe is available as a binary, as a library for C++, Python, Perl, Java, C#, and as a web service.
UDPipe is a free software under 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 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/).
UDPipe website http://ufal.mff.cuni.cz/udpipe contains download links of both the released packages and trained models, hosts documentation and offers online demo.
UDPipe development repository http://github.com/ufal/udpipe is hosted on GitHub.
UDPipe 2 is a POS tagger, lemmatizer and dependency parser.
Compared to UDPipe 1:
- UDPipe 2 is Python-only and tested only in Linux,
- UDPipe 2 is meant as a research tool, not as a user-friendly UDPipe 1 replacement,
- UDPipe 2 achieves much better performance, but requires a GPU for reasonable performance,
- UDPipe 2 does not perform tokenization by itself – it uses UDPipe 1 for that.
UDPipe 2 is available in the udpipe-2 branch of the UDPipe repository at https://github.com/ufal/udpipe/tree/udpipe-2. It is a free software under Mozilla Public License 2.0 (http://www.mozilla.org/MPL/2.0/) and the models are free for non-commercial use and distributed under 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 2 is also available as a REST service running at https://lindat.mff.cuni.cz/services/udpipe. If you like, you can use the https://github.com/ufal/udpipe/blob/udpipe-2/udpipe2_client.py script to interact with it.
UDPipe is a trainable pipeline for tokenizing, tagging, lemmatizing and parsing Universal Treebanks and other CoNLL-U files (https://lindat.mff.cuni.cz/services/udpipe/)
UDPipe is a trainable pipeline for tokenizing, tagging, lemmatizing and parsing Universal Treebanks and other CoNLL-U files (https://lindat.mff.cuni.cz/services/udpipe/)
UDPipe is a trainable pipeline for tokenizing, tagging, lemmatizing and parsing Universal Treebanks and other CoNLL-U files (https://lindat.mff.cuni.cz/services/udpipe/)
UDPipe is a trainable pipeline for tokenizing, tagging, lemmatizing and parsing Universal Treebanks and other CoNLL-U files (https://lindat.mff.cuni.cz/services/udpipe/)
This is the first release of the UFAL Parallel Corpus of North Levantine, compiled by the Institute of Formal and Applied Linguistics (ÚFAL) at Charles University within the Welcome project (https://welcome-h2020.eu/). The corpus consists of 120,600 multiparallel sentences in English, French, German, Greek, Spanish, and Standard Arabic selected from the OpenSubtitles2018 corpus [1] and manually translated into the North Levantine Arabic language. The corpus was created for the purpose of training machine translation for North Levantine and the other languages.
The corpus contains recordings by the native speakers of the North Levantine Arabic (apc) acquired during 2020, 2021, and 2023 in Prague, Paris, Kabardia, and St. Petersburg. Altogether, there were 13 speakers (9 male and 4 female, aged 1x 15-20, 7x 20-30, 4x 30-40, and 1x 40-50).
The recordings contain both monologues and dialogues on the topics of everyday life (health, education, family life, sports, culture) as well as information on both host countries (living abroad) and country of origin (Syria traditions, education system, etc.). Both types are spontaneous, the participants were given only the general subject and talked on the topic or discussed it freely. The transcription and translation team consisted of students of Arabic at Charles University, with an additional quality check provided by the native speakers of the dialect.
The textual data is split between the (parallel) transcriptions (.apc) and translations (.eng), with one segment per line. The additional .yaml file provides mapping to the corresponding audio file (with the duration and offset in the "%S.%03d" format, i.e., seconds and milliseconds) and a unique speaker ID.
The audio data is shared in the 48kHz .wav format, with dialogues and monologues in separate folders. All of the recordings are mono, with a single channel. For dialogues, there is a separate file for each speaker, e.g., "Tar_13052022_Czechia-01.wav" and "Tar_13052022_Czechia-02.wav".
The data provided in this repository corresponds to the validation split of the dialectal Arabic to English shared task hosted at the 21st edition of the International Conference on Spoken Language Translation, i.e., IWSLT 2024.
The corpus contains recordings by the native speakers of the North Levantine Arabic (apc) acquired during 2020, 2021, and 2023 in Prague, Paris, Kabardia, and St. Petersburg. Altogether, there were 13 speakers (9 male and 4 female, aged 1x 15-20, 7x 20-30, 4x 30-40, and 1x 40-50).
The recordings contain both monologues and dialogues on the topics of everyday life (health, education, family life, sports, culture) as well as information on both host countries (living abroad) and country of origin (Syria traditions, education system, etc.). Both types are spontaneous, the participants were given only the general subject and talked on the topic or discussed it freely. The transcription and translation team consisted of students of Arabic at Charles University, with an additional quality check provided by the native speakers of the dialect.
The textual data is split between the (parallel) transcriptions (.apc) and translations (.eng), with one segment per line. The additional .yaml file provides mapping to the corresponding audio file (with the duration and offset in the "%S.%03d" format, i.e., seconds and milliseconds) and a unique speaker ID.
The audio data is shared in the 48kHz .wav format, with dialogues and monologues in separate folders. All of the recordings are mono, with a single channel. For dialogues, there is a separate file for each speaker, e.g., "16072022_Family-01.wav" and "16072022_Family-02.wav".
The data provided in this repository corresponds to the test split of the dialectal Arabic to English shared task hosted at the 21st edition of the International Conference on Spoken Language Translation, i.e., IWSLT 2024.