uk-ner
| Property | Value |
|---|---|
| Author | ukr-models |
| Base Architecture | XLM-RoBERTa |
| Task | Named Entity Recognition |
| Language | Ukrainian |
| Model Hub | Hugging Face |
What is uk-ner?
uk-ner is a specialized Named Entity Recognition model fine-tuned on a synthetic Ukrainian dataset. Built upon the XLM-RoBERTa architecture, it's specifically designed to identify and classify named entities in Ukrainian text, including persons (PER), locations (LOC), and organizations (ORG) using the B-I tagging scheme.
Implementation Details
The model implements a token classification approach using the powerful XLM-RoBERTa base model. It utilizes a B-I (Beginning-Inside) tagging system for precise entity boundary detection, supporting six distinct tags: B-PER, I-PER, B-LOC, I-LOC, B-ORG, and I-ORG.
- Built on XLM-RoBERTa architecture optimized for Ukrainian language
- Supports both token-level and word-level predictions
- Implements B-I tagging scheme for accurate entity boundary detection
- Includes custom preprocessing using tokenize_uk package
Core Capabilities
- Identifies person names with B-PER and I-PER tags
- Detects location entities using B-LOC and I-LOC tags
- Recognizes organizations through B-ORG and I-ORG tags
- Processes Ukrainian text with high accuracy
- Supports both pipeline and custom implementation approaches
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Ukrainian language NER tasks, utilizing a synthetic dataset and implementing a comprehensive B-I tagging system for precise entity recognition. It's one of the few models specifically designed for Ukrainian named entity recognition.
Q: What are the recommended use cases?
The model is ideal for applications requiring named entity extraction from Ukrainian text, such as information extraction systems, content analysis tools, and automated document processing. It's particularly useful for identifying persons, locations, and organizations in Ukrainian documents.
