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• The CLASSLA-Stanza model for UD dependency parsing of standard Serbian 2.1

  3 resources

  The model for UD dependency parsing of standard Serbian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) 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/11356/1789). The estimated LAS of the parser is ~89.83. The difference to the previous version of the model is that this version uses the new version of Serbian word embeddings.

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

  2 resources

  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.

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

  3 resources

  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.

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• RÚV-DI Speaker Diarization (21.10)

  1 resources

  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.

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• BlogReader

  1 resources

  BlogReader - corpus acquisition from structured web sources

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• Samrómur DeepSpeech Recipe 22.06

  2 resources

  The "Samrómur DeepSpeech Recipe 22.06" is a code recipe intended to show how to integrate the corpus "Samromur 21.05" [1] and the "DeepSpeech Scorer for Icelandic 22.06" [2] to create automatic speech recognition systems using the Mozilla's DeepSpeech recognizer [3].

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• Samrómur-Adolescents Kaldi Recipe 22.06

  2 resources

  The "Samrómur-Adolescents Kaldi Recipe 22.06" is a code recipe intended to show how to integrate the adolescent portion of the corpus "Samrómur Children's Icelandic Speech Data 21.09" [1] and the "Icelandic Language Models with Pronunciations 22.01" [2] to create automatic speech recognition systems using the Kaldi toolkit [3].

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

  3 resources

  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).

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

  3 resources

  The model for morphosyntactic annotation 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 CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~94.1. 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).

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• The CLASSLA-StanfordNLP model for lemmatisation of standard Croatian 1.2

  2 resources

  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 is that now it relies solely on XPOS annotations, and not on a combination of UPOS, FEATS (lexicon lookup) and XPOS (lemma prediction) annotations.

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