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dchaplinsky
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uk_ner_web_trf_large

Token Classification
spaCy
Ukrainian
Eval Results
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  ## Model description
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- **roberta-uk-ner-large** is a fine-tuned [XLM-Roberta model](https://huggingface.co/xlm-roberta-base) that is ready to use for **Named Entity Recognition** and achieves a **SoA** performance for the NER task for Ukrainian language. It outperforms another SpaCy model, [uk_core_news_trf](https://huggingface.co/ukr-models/uk_core_news_trf) on a NER task I.t has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PERS) and Miscellaneous (MISC).
 
 
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  The model was fine-tuned on the [NER-UK dataset](https://github.com/lang-uk/ner-uk), released by the [lang-uk](https://lang.org.ua).
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  ## Model description
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+ **roberta-uk-ner-large** is a fine-tuned [XLM-Roberta model](https://huggingface.co/xlm-roberta-large) that is ready to use for **Named Entity Recognition** and achieves a **SoA** performance for the NER task for Ukrainian language. It outperforms another SpaCy model, [uk_core_news_trf](https://huggingface.co/ukr-models/uk_core_news_trf) on a NER task.
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+ It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PERS) and Miscellaneous (MISC).
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  The model was fine-tuned on the [NER-UK dataset](https://github.com/lang-uk/ner-uk), released by the [lang-uk](https://lang.org.ua).
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