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

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  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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• PyTorch model for Slovenian Named Entity Recognition SloNER 1.0

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  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.

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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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• Icelandic NER API - Ensamble model (21.09)

  2 resources

  A dockerized Named Entity Recognition (NER) API for Icelandic. It uses a the IceBERT language model from Miðeind as its primary model, but it also offers the possibility to use 3 other transformer language models with it ( ELECTRA-base, convbert-small, and multilingual-BERT) and combines them with CombiTagger. They were all fine tuned for NER using MIM-GOLD-NER. IceBERT was the best individual model as it achieves F1-score of ~92.73 on the test set for MIM-GOLD-NER, while the combination of the four, in the form of CombiTagger, achieved F1-score of 93.21. The code for the API is available at https://github.com/icelandic-lt/Icelandic-NER-API and the files for the fine tuned models are available in this submission. Dockerútfærð forritaskil fyrir nafnakennsl (NER) á íslensku. Þau notast við IceBERT mállíkan frá Miðeind sem sitt megin líkan, en þau bjóða líka upp á möguleikann að láta IceBERT vinna með 3 öðrum líkönum (ELECTRA-base, convbert-small og multilingual-BERT). Þau hafa öll verið fínstillt fyrir NER með nafnakennslamálheildinni MIM-GOLD-NER. Ef við skoðum hvert líkan fyrir sig, þá er IceBERT líkanið best, en það nær 92.73 í F1, á meðn CombiTagger nær 93.21 í F1. Forritunarkóðinn fyrir forritaskilinu eru aðgengileg hérna: https://github.com/icelandic-lt/Icelandic-NER-API og skrárnar fyrir fínstilltu líkönin má finna í þessari færslu.

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