small-e-czech-finetuned-ner-wikiann
| Property | Value |
|---|---|
| Base Model | Seznam/small-e-czech |
| Task | Named Entity Recognition (NER) |
| Dataset | WikiAnn |
| Performance | F1: 0.8840, Accuracy: 0.9557 |
| Author | richielo |
What is small-e-czech-finetuned-ner-wikiann?
This is a specialized Named Entity Recognition model fine-tuned specifically for the Czech language. Built upon Seznam's small-e-czech architecture, it has been optimized using the WikiAnn dataset to achieve impressive performance metrics in identifying and classifying named entities in Czech text.
Implementation Details
The model was trained using a carefully crafted process over 20 epochs, utilizing the Adam optimizer with a learning rate of 2e-05. Training was conducted with batch sizes of 8 for both training and evaluation, achieving consistent improvement in performance metrics throughout the training process.
- Training Duration: 20 epochs
- Batch Size: 8
- Optimizer: Adam (betas=0.9,0.999)
- Learning Rate: 2e-05
- Final Metrics: Precision (0.8713), Recall (0.8970), F1 (0.8840)
Core Capabilities
- High-accuracy Named Entity Recognition for Czech text
- Robust performance with 95.57% accuracy
- Balanced precision and recall metrics
- Optimized for production deployment with small model footprint
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Czech language NER tasks, achieving state-of-the-art performance metrics while maintaining efficiency through its small-e-czech foundation. The balanced precision-recall trade-off makes it particularly suitable for production applications.
Q: What are the recommended use cases?
The model is ideal for applications requiring Czech named entity recognition, including information extraction, content analysis, and automated document processing. Its high accuracy and F1 score make it suitable for both research and production environments.
