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• KPWr n82 NER model (on Polish RoBERTa base)

  2 resources

  The named entity recognition model for fine-grained categories of entities (82 types) was trained on the KPWr corpus using Polish RoBERTa base language model. Details can be found on the following page: https://github.com/mczuk/xlm-roberta-ner

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• Liner2.6 model NER NKJP

  3 resources

  Liner2.6 NER NKJP model The package contains a pre-trained Liner2 (https://github.com/CLARIN-PL/Liner2) model for recognition named entities according to NKJP guidelines. The model was trained on the NKJP corpus (http://nkjp.pl/) and evaluated in the PolEval 2018 Task 2 (http://poleval.pl/tasks/). The model won third place with the following results: Exact — 0.778, Overlap — 0.818, Final — 0.810. References: * NKJP corpus in TEI format — http://clip.ipipan.waw.pl/NationalCorpusOfPolish?action=AttachFile&do=view&target=NKJP-PodkorpusMilionowy-1.2.tar.gz * PolEval 2018 Task 2 evaluation corpus — http://mozart.ipipan.waw.pl/~axw/poleval2018/

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