| | --- |
| | license: apache-2.0 |
| | language: |
| | - uk |
| | metrics: |
| | - f1 |
| | - precision |
| | - recall |
| | base_model: |
| | - 51la5/roberta-large-NER |
| | pipeline_tag: token-classification |
| | library_name: spacy |
| | model-index: |
| | - name: roberta-large-ner-uk |
| | results: |
| | - task: |
| | name: NER |
| | type: token-classification |
| | metrics: |
| | - name: NER Precision |
| | type: precision |
| | value: 0.9468 |
| | - name: NER Recall |
| | type: recall |
| | value: 0.9416 |
| | - name: NER F1 |
| | type: f1 |
| | value: 0.9442 |
| | tags: |
| | - ner |
| | - uk |
| | datasets: |
| | - lang-uk/UberText-NER-Silver |
| | --- |
| | # roberta-large-ner-uk |
| |
|
| | A transformer-based NER model for Ukrainian, trained on a combination of human-annotated data (NER-UK 2.0) and high-quality silver-standard annotations (UberText-NER-Silver). Based on `roberta-large-NER`, this model achieves state-of-the-art performance on a wide range of named entities in Ukrainian. |
| |
|
| | ## Model Details |
| |
|
| | - **Model type:** Transformer-based encoder (spaCy pipeline) |
| | - **Language (NLP):** Ukrainian |
| | - **License:** Apache 2.0 |
| | - **Finetuned from model:** `51la5/roberta-large-NER` |
| | - **Entity Types (13):** `PERS`, `ORG`, `LOC`, `DATE`, `TIME`, `JOB`, `MON`, `PCT`, `PERIOD`, `DOC`, `QUANT`, `ART`, `MISC` |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | import spacy |
| | nlp = spacy.load("roberta-large-ner-uk") |
| | doc = nlp("Президент України Володимир Зеленський виступив у Брюсселі.") |
| | print([(ent.text, ent.label_) for ent in doc.ents]) |
| | ``` |
| |
|
| | ## Authors |
| |
|
| | [Vladyslav Radchenko](https://huggingface.co/pofce), [Nazarii Drushchak](https://huggingface.co/ndrushchak) |
