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

  2 resources

  This model for named entity recognition of non-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), the ReLDI-NormTagNER-hr corpus (http://hdl.handle.net/11356/1241) and the ReLDI-NormTagNER-sr corpus (http://hdl.handle.net/11356/1240), using the CLARIN.SI-embed.hr word embeddings (http://hdl.handle.net/11356/1205). 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 named entity recognition of non-standard Slovenian 1.0

  2 resources

  This model for named entity recognition of non-standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1210) and the Janes-Tag training corpus (http://hdl.handle.net/11356/1238), using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204). 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 named entity recognition of standard Bulgarian 1.0

  3 resources

  This model for named entity recognition of standard Bulgarian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the BulTreeBank training corpus (http://hdl.handle.net/11495/D93F-C6E9-65D9-2) and using the CoNLL2017 word embeddings (http://hdl.handle.net/11234/1-1989).

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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 named entity recognition of standard Croatian 1.0

  3 resources

  This model for named entity recognition 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).

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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 named entity recognition of standard Slovenian 2.2

  3 resources

  This model for named entity recognition 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/11356/1959) and using the CLARIN.SI-embed.sl 2.0 word embeddings (http://hdl.handle.net/11356/1791). The difference to the previous version of the model is that the model was trained using the SUK training corpus and uses new embeddings.

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

  3 resources

  This model for named entity recognition of standard Slovenian was built with the CLASSLA-StanfordNLP tool (https://github.com/clarinsi/classla-stanfordnlp) by training on the ssj500k training corpus (http://hdl.handle.net/11356/1210) and using the CLARIN.SI-embed.sl word embeddings (http://hdl.handle.net/11356/1204).

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• IceEval - Icelandic Natural Language Processing Benchmark 22.09

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  IceEval is a benchmark for evaluating and comparing the quality of pre-trained language models. The models are evaluated on a selection of four NLP tasks for Icelandic: part-of-speech tagging (using the MIM-GOLD corpus), named entity recognition (using the MIM-GOLD-NER corpus), dependency parsing (using the IcePaHC-UD corpus) and automatic text summarization (using the IceSum corpus). IceEval includes scripts for downloading the datasets, splitting them into training, validation and test splits and training and evaluating models for each task. The benchmark uses the Transformers, DiaParser and TransformerSum libraries for fine-tuning and evaluation. IceEval er tól til að meta og bera saman forþjálfuð mállíkön. Líkönin eru metin á fjórum máltækniverkefnum fyrir íslensku: mörkun (með MIM-GOLD málheildinni), nafnakennslum (með MIM-GOLD-NER málheildinni), þáttun (með IcePaHC-UD málheildinni) og sjálfvirkri samantekt (með IceSum málheildinni). IceEval inniheldur skriftur til að sækja gagnasöfnin, skipta þeim í þjálfunar- og prófunargögn og að fínstilla og meta líkön fyrir hvert verkefni. Transformers, DiaParser og TransformerSum forritasöfnin eru notuð til að fínstilla líkönin.

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