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

  2 resources

  The model for lemmatisation 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) and the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1794), 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 ~94.92. The difference to the previous version of the model is that this version is trained on a combination of two corpora (SETimes.SR, ReLDI-NormTagNER-sr).

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• Universal Dependencies 2.10 models for UDPipe 2 (2022-07-11)

  2 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for 123 treebanks of 69 languages of Universal Depenencies 2.10 Treebanks, created solely using UD 2.10 data (https://hdl.handle.net/11234/1-4758). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_210_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

  Use "Universal Dependencies 2.10 models for UDPipe 2 (2022-07-11)"

• 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.

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

• 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 .

  Use "Universal Dependencies 2.15 models for UDPipe 2 (2024-11-21)"

• 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 .

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

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

  2 resources

  The model for lemmatisation 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 srLex inflectional lexicon (http://hdl.handle.net/11356/1233). The estimated F1 of the lemma annotations is ~97.9. 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.

  Use "The CLASSLA-StanfordNLP model for lemmatisation of standard Serbian 1.1"

• The CLASSLA-Stanza model for lemmatisation of standard Serbian 2.1

  2 resources

  The model for lemmatisation 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 Serbian non-standard training corpus ReLDI-NormTagNER-sr (http://hdl.handle.net/11356/1794) and using the srLex inflectional lexicon (http://hdl.handle.net/11356/1233). The estimated F1 of the lemma annotations is ~98.02. The difference to the previous version is that this version was trained on a combination of the standard (SETimes.SR) and non-standard (ReLDI-NormTagNER-sr) Serbian training corpora.

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• Universal Dependencies 2.4 Models for UDPipe (2019-05-31)

  93 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for 90 treebanks of 60 languages of Universal Depenencies 2.4 Treebanks, created solely using UD 2.4 data (http://hdl.handle.net/11234/1-2988). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_24_models . To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe . In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.

  Use "Universal Dependencies 2.4 Models for UDPipe (2019-05-31)"

• Universal Dependencies 2.6 models for UDPipe 2 (2020-08-31)

  2 resources

  Tokenizer, POS Tagger, Lemmatizer and Parser models for 99 treebanks of 63 languages of Universal Depenencies 2.6 Treebanks, created solely using UD 2.6 data (https://hdl.handle.net/11234/1-3226). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_26_models . To use these models, you need UDPipe version 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .

  Use "Universal Dependencies 2.6 models for UDPipe 2 (2020-08-31)"

• The CLASSLA-StanfordNLP model for lemmatisation of standard Serbian

  2 resources

  The model for lemmatisation 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 srLex inflectional lexicon (http://hdl.handle.net/11356/1233). The estimated F1 of the lemma annotations is ~97.9.

  Use "The CLASSLA-StanfordNLP model for lemmatisation of standard Serbian"