Result filters

Metadata provider

Language

...more

Resource type

Availability

703 record(s) found

Search results

  • The CLASSLA-Stanza model for JOS dependency parsing of standard Slovenian 2.0

    This model for JOS dependency parsing of standard Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SUK training corpus (http://hdl.handle.net/11356/1747) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204) expanded with the MaCoCu-sl Slovene web corpus (http://hdl.handle.net/11356/1517). The estimated LAS of the parser is ~93.89. The difference to the previous version of the model is that the model was trained using the SUK training corpus and uses the updated embeddings.

  • The CLASSLA-StanfordNLP model for morphosyntactic annotation of standard Serbian 1.1

    The model for morphosyntactic annotation of standard Serbian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the SETimes.SR training corpus (http://hdl.handle.net/11356/1200) and using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1206). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~95.2. The difference to the previous version of the model is that now the whole XPOS tag is predicted and not specific characters, as was the case in stanfordnlp, which resulted in illegal XPOS tags (and slightly decreased performance).

  • Semi-supervised Icelandic-Polish Translation System (22.09)

    This Icelandic-Polish translation model (bi-directional) was trained using fairseq (https://github.com/facebookresearch/fairseq) by means of semi-supervised translation by starting with the mBART50 model. The model was then trained using a multi-task curriculum to first learn to denoise sentences. Then the model was trained to translate using aligned parallel texts. Finally the model was provided with monolingual texts in both Icelandic and Polish with which it iteratively creates back-translations. For the PL-IS direction the model achieves a BLEU score of 27.60 on held out true parallel training data and 15.30 on the out-of-domain Flores devset. For the IS-PL direction the model achieves a score of 27.70 on the true data and 13.30 on the Flores devset. -- Þetta íslensk-pólska þýðingarlíkan (tvíátta) var þjálfað með fairseq (https://github.com/facebookresearch/fairseq) með hálf-sjálfvirkum aðferðum frá mBART50 líkaninu. Líkanið var þjálfað á þremur verkefnum, afruglun, samhliða þýðingum og bakþýðingum sem voru myndaðar á þjálfunartíma. Fyrir PL-IS áttina fæst BLEU skor 27.60 á raun gögnum sem voru tekin til hliðar og 15.30 á Flores þróunargögnunum. Fyrir IS-PL áttina fæst skor 27.70 á raun gögnunum og 13.30 á Flores þróunargögnunum.

  • RÚV-DI Speaker Diarization (21.10)

    These are a set of speaker diarization recipes which depend on the speech toolkit Kaldi. There are two types of recipes here. First are recipes used for decoding unseen audio. The second type of recipes are for training diarization models on the Rúv-di data. This tool also lists the DER for the Rúv-di dataset on most of the recipes. All DERs within this tool have no unscored collars and include overlapping speech Þessi pakki inniheldur forskriftir fyrir samræðugreind fyrir hugbúnaðarumhverfið Kaldi. Pakkinn inniheldur tvær tegundir af forskriftum. Annars vegar forskriftir sem greina samræður í nýjum hljóðskrám og hins vegar forskriftir til að þjálfa ný samræðugreindarlíkön með Rúv-di-gagnasafninu. Hluti forskriftanna innihalda villutíðni (DER) fyrir Rúv-di-gagnasettið.

  • The CLASSLA-StanfordNLP model for lemmatisation of standard Croatian 1.1

    The model for lemmatisation of standard Croatian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the hr500k training corpus (http://hdl.handle.net/11356/1183) and using the hrLex inflectional lexicon (http://hdl.handle.net/11356/1232). The estimated F1 of the lemma annotations is ~97.6. The difference to the previous version of the model is that it is trained with the lemmatiser padding bug removed, cf. https://github.com/stanfordnlp/stanfordnlp/issues/143.