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  • Language: Slovenian
  • Project: Development of Slovene in a Digital Environment
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  • The CLASSLA-Stanza model for lemmatisation of standard Slovenian 2.0

    This model for lemmatisation 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 F1 of the lemma annotations is ~99.11. The difference to the previous version of the model is that the model was trained using the SUK training corpus and uses new embeddings and the new version of the Slovene morphological lexicon Sloleks 3.0 (http://hdl.handle.net/11356/1745).

  • The CLASSLA-Stanza model for semantic role labeling of standard Slovenian 2.0

    The model for semantic role labeling 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) extended with the MaCoCu-sl Slovenian web corpus (http://hdl.handle.net/11356/1517). The estimated F1 of the semantic role annotations is ~76.24. The difference to the previous version of the model is that the model was trained using the SUK training corpus and the updated word embeddings.

  • The CLASSLA-Stanza model for morphosyntactic annotation of spoken Slovenian 2.2

    This model for morphosyntactic annotation of spoken Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SST treebank of spoken Slovenian (https://github.com/UniversalDependencies/UD_Slovenian-SST) combined with the SUK training corpus (http://hdl.handle.net/11356/1959) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1791) that were expanded with the MaCoCu-sl Slovene web corpus (http://hdl.handle.net/11356/1517). The model produces simultaneously UPOS, FEATS and XPOS (MULTEXT-East) labels. The estimated F1 of the XPOS annotations is ~96.76.

  • The CLASSLA-Stanza model for lemmatisation of spoken Slovenian 2.2

    This model for lemmatisation of spoken Slovenian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SST treebank of spoken Slovenian (https://github.com/UniversalDependencies/UD_Slovenian-SST) combined with the SUK training corpus (http://hdl.handle.net/11356/1959) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1791) that were expanded with the MaCoCu-sl Slovene web corpus (http://hdl.handle.net/11356/1517). The estimated F1 of the lemma annotations is ~99.23.

  • PyTorch model for Slovenian Coreference Resolution

    Slovenian model for coreference resolution: a neural network based on a customized transformer architecture, usable with the code published on https://github.com/matejklemen/slovene-coreference-resolution. The model is based on the Slovenian CroSloEngual BERT 1.1 model (http://hdl.handle.net/11356/1330). It was trained on the SUK 1.0 training corpus (http://hdl.handle.net/11356/1747), specifically the SentiCoref subcorpus. Using the evaluation setting where entity mentions are assumed to be correctly pre-detected, the model achieves the following metric values: MUC: precision = 0.931, recall = 0.957, F1 = 0.943 BCubed: precision = 0.887, recall = 0.947, F1 = 0.914 CEAFe: precision = 0.945, recall = 0.893, F1 = 0.916 CoNLL-12: precision = 0.921, recall = 0.932, F1 = 0.924

  • CORDEX inflectional lookup data 1.0

    The inflectional data lookup module serves as an optional component within the cordex library (https://github.com/clarinsi/cordex/) that significantly improves the quality of the results. The module consists of a pickled dictionary of 111,660 lemmas, and maps these lemmas to their corresponding word forms. Each word form in the dictionary is accompanied by its MULTEXT-East morphosytactic descriptions, relevant features (custom features extracted from morphosytactic descriptions with the help of https://gitea.cjvt.si/generic/conversion_utils and its frequency within the Gigafida 2.0 corpus (http://hdl.handle.net/11356/1320), or Gigafida 1.0 when other information is unavailable. The dictionary is used to select the most frequent word form of a lemma that satisfies additional filtering conditions (ie. find the most utilized word form of lemma "centralen" in singular, i.e."centralni").

  • 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.

  • Slovenian text summarization models

    A text summarisation task aims to convert a longer text into a shorter text while preserving the essential information of the source text. In general, there are two approaches to text summarization. The extractive approach simply rewrites the most important sentences or parts of the text, whereas the abstractive approach is more similar to human-made summaries. We release 5 models that cover extractive, abstractive, and hybrid types: Metamodel: a neural model based on the Doc2Vec document representation that suggests the best summariser. Graph-based model: unsupervised graph-based extractive approach that returns the N most relevant sentences. Headline model: a supervised abstractive approach (T5 architecture) that returns returns headline-like abstracts. Article model: a supervised abstract approach (T5 architecture) that returns short summaries. Hybrid-long model: unsupervised hybrid (graph-based and transformer model-based) approach that returns short summaries of long texts. Details and instructions to run and train the models are available at https://github.com/clarinsi/SloSummarizer. The web service with a demo is available at https://slovenscina.eu/povzemanje.

  • PyTorch model for Slovenian Named Entity Recognition SloNER 1.0

    The SloNER is a model for Slovenian Named Entity Recognition. It is is a PyTorch neural network model, intended for usage with the HuggingFace transformers library (https://github.com/huggingface/transformers). The model is based on the Slovenian RoBERTa contextual embeddings model SloBERTa 2.0 (http://hdl.handle.net/11356/1397). The model was trained on the SUK 1.0 training corpus (http://hdl.handle.net/11356/1747).The source code of the model is available on GitHub repository https://github.com/clarinsi/SloNER.

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

    This model for UD 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 ~91.11. 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.