--- tags: - flair - token-classification - sequence-tagger-model language: uk datasets: - ner-uk model-index: - name: flair-uk-ner results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.8616 - name: NER Recall type: recall value: 0.8593 - name: NER F Score type: f_score value: 0.8605 widget: - text: "Президент Володимир Зеленський пояснив, що наразі діалог із режимом Володимира путіна неможливий, адже агресор обрав курс на знищення українського народу. За словами Зеленського цей режим РФ виявляє неповагу до суверенітету і територіальної цілісності України." license: mit --- # flair-uk-ner ## Model description **flair-uk-ner** is a Flair model that is ready to use for **Named Entity Recognition**. It is based on flair embeddings, that I've trained for Ukrainian language (available [here](https://huggingface.co/dchaplinsky/flair-uk-backward) and [here](https://huggingface.co/dchaplinsky/flair-uk-forward)) and has nice performance and a very **small size** (just 72mb!). It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PERS) and Miscellaneous (MISC). Results: - F-score (micro) **0.8605** - F-score (macro) **0.7472** - Accuracy **0.8033** | by class | precision | recall | f1-score | support | |--------------|-----------|--------|----------|---------| | **PERS** | 0.9305 | 0.9422 | 0.9363 | 1678 | | **LOC** | 0.8150 | 0.8678 | 0.8406 | 401 | | **ORG** | 0.6653 | 0.6092 | 0.6360 | 261 | | **MISC** | 0.6202 | 0.5375 | 0.5759 | 240 | | micro avg | 0.8616 | 0.8593 | 0.8605 | 2580 | | macro avg | 0.7577 | 0.7392 | 0.7472 | 2580 | | weighted avg | 0.8569 | 0.8593 | 0.8575 | 2580 | 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). Training code is also available [here](https://github.com/lang-uk/flair-ner). Copyright: [Dmytro Chaplynskyi](https://twitter.com/dchaplinsky), [lang-uk project](https://lang.org.ua), 2022