BERT Fine-tuned NER Model
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Base Model | bert-base-cased |
| Task | Token Classification (NER) |
| Dataset | CoNLL-2003 |
What is bert-finetuned-ner?
This is a specialized Named Entity Recognition model built on top of BERT-base-cased architecture, fine-tuned specifically on the CoNLL-2003 dataset. The model demonstrates exceptional performance with a 92.22% F1 score on the test set, making it highly reliable for identifying and classifying named entities in text.
Implementation Details
The model was trained using a carefully optimized process with Adam optimizer, utilizing a learning rate of 2e-05 and linear scheduling. Training was conducted over 3 epochs with batch sizes of 8 for both training and evaluation.
- Precision: 92.87%
- Recall: 91.58%
- F1 Score: 92.22%
- Accuracy: 90.04%
Core Capabilities
- High-accuracy named entity recognition
- Token-level classification
- Optimized for English language processing
- Suitable for production environments
Frequently Asked Questions
Q: What makes this model unique?
The model achieves exceptional performance metrics, particularly its balanced precision-recall trade-off, making it especially reliable for real-world NER applications. The verified metrics and rigorous evaluation process ensure its reliability.
Q: What are the recommended use cases?
This model is ideal for applications requiring named entity recognition in formal text, such as information extraction systems, document analysis, and automated content categorization. It's particularly effective for identifying entities in news articles and formal documents, similar to the CoNLL-2003 dataset it was trained on.
