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• The CLASSLA-StanfordNLP model for named entity recognition of standard Serbian 1.0

  3 resources

  This model for named entity recognition 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).

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• The CLASSLA-Stanza model for morphosyntactic annotation of standard Serbian 2.1

  3 resources

  The model for morphosyntactic annotation 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) combined with the Croatian hr500k training dataset (http://hdl.handle.net/11356/1792) to ensure sufficient representation of certain labels. The CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1789) were used during training. The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~96.19. The difference to the previous version of the model is that this version was trained on the SETimes.SR corpus expanded with the Croatian hr500k training dataset to ensure sufficient representation of certain labels. it was also trained using the new version of Serbian word embeddings.

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• The CLASSLA-Stanza model for morphosyntactic annotation of non-standard Serbian 2.1

  3 resources

  This model for morphosyntactic annotation of non-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), the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1794) and the hr500k training corpus (http://hdl.handle.net/11356/1792), using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1789). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~92.64. The difference to the previous version of the model is that this version uses the new version of Serbian word embeddings and is trained on a combination of three training corpora (SETimes.SR, ReLDI-NormTagNER-sr, hr500k).

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• UDify Pretrained Model

  3 resources

  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.

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• The CLASSLA-StanfordNLP model for named entity recognition of non-standard Serbian 1.0

  2 resources

  This model for named entity recognition of non-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), the hr500k training corpus (http://hdl.handle.net/11356/1183), the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240) and the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241), using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1206). The training corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed.

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• The CLASSLA-StanfordNLP model for lemmatisation of non-standard Serbian 1.1

  2 resources

  The model for lemmatisation of non-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), the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240), the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241), the hr500k training corpus (http://hdl.handle.net/11356/1183) and the RAPUT corpus (https://www.aclweb.org/anthology/L16-1513/), using the srLex inflectional lexicon (http://hdl.handle.net/11356/1233). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The estimated F1 of the lemma annotations is ~97.62. The difference to the previous version of the lemmatizer 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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• Universal Dependencies 2.15 models for UDPipe 2 (2024-11-21)

  2 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for 147 treebanks of 78 languages of Universal Depenencies 2.15 Treebanks, created solely using UD 2.15 data (https://hdl.handle.net/11234/1-5787). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_215_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

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• Universal Dependencies 2.12 models for UDPipe 2 (2023-07-17)

  2 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for 131 treebanks of 72 languages of Universal Depenencies 2.12 Treebanks, created solely using UD 2.12 data (https://hdl.handle.net/11234/1-5150). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_212_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

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• The CLASSLA-StanfordNLP model for morphosyntactic annotation of non-standard Serbian 1.0

  3 resources

  This model for morphosyntactic annotation of non-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), the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240), the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241), the hr500k training corpus (http://hdl.handle.net/11356/1210) and the RAPUT corpus (https://www.aclweb.org/anthology/L16-1513/), using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1206). These corpora were additionally augmented for handling missing diacritics by repeating parts of the corpora with diacritics removed. The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~94.91.

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

  3 resources

  The model for UD dependency parsing 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 estimated LAS of the parser is ~89.0.

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