xlm-roberta-base-romanian-ner-ronec
| Property | Value |
|---|---|
| Author | EvanD |
| Task | Named Entity Recognition |
| Language | Romanian |
| F1-Macro Score | 95.48% |
| Base Architecture | XLM-RoBERTa |
What is xlm-roberta-base-romanian-ner-ronec?
This is a specialized Named Entity Recognition (NER) model built on the XLM-RoBERTa architecture, specifically trained for Romanian language processing. The model demonstrates exceptional performance with a 95.48% F1-macro score on the RONEC dataset, making it a powerful tool for identifying and classifying named entities in Romanian text.
Implementation Details
The model is implemented using the Transformers library and can be easily integrated into existing NLP pipelines. It uses the XLM-RoBERTa base architecture as its foundation, which has been fine-tuned on the RONEC dataset for optimal performance in Romanian NER tasks.
- Test F1-Macro Score: 0.9547
- Test Precision: 0.8664
- Test Recall: 0.8696
- Test Loss: 0.1637
Core Capabilities
- Accurate identification of named entities in Romanian text
- Support for multiple entity types including persons (PER) and locations (GPE)
- Simple integration using the Transformers pipeline
- High-confidence predictions with scores typically above 0.99
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional performance on Romanian NER tasks, achieving a 95.48% F1-macro score. It's specifically optimized for the Romanian language and can accurately identify various entity types with high confidence scores.
Q: What are the recommended use cases?
The model is ideal for applications requiring named entity recognition in Romanian text, such as information extraction, document analysis, and automated text processing systems. It's particularly effective at identifying person names and geographical locations with high accuracy.
