bert-large-cased-finetuned-conll03-english
| Property | Value |
|---|---|
| Model Type | BERT Large Cased |
| Task | Named Entity Recognition (NER) |
| Dataset | CoNLL-03 |
| Author | dbmdz |
| Model URL | Hugging Face Hub |
What is bert-large-cased-finetuned-conll03-english?
This is a specialized version of BERT-large-cased that has been fine-tuned specifically for Named Entity Recognition (NER) using the CoNLL-03 English dataset. The model is designed to identify and classify named entities in text, including persons, organizations, locations, and miscellaneous entities.
Implementation Details
The model builds upon the BERT-large-cased architecture, which maintains case sensitivity crucial for NER tasks. It has been fine-tuned on the CoNLL-03 dataset, a benchmark dataset for NER containing annotations for four entity types.
- Based on BERT-large-cased architecture
- Maintains case sensitivity for better entity recognition
- Fine-tuned on CoNLL-03 English dataset
- Optimized for production use in NER tasks
Core Capabilities
- Recognition of Person names (PER)
- Identification of Organization names (ORG)
- Detection of Location references (LOC)
- Classification of Miscellaneous entities (MISC)
- Processing of case-sensitive input text
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful BERT-large architecture with specific fine-tuning for NER tasks, making it particularly effective for recognizing named entities in English text while maintaining case sensitivity, which is crucial for accurate entity recognition.
Q: What are the recommended use cases?
The model is ideal for applications requiring named entity extraction from English text, such as information extraction systems, content analysis tools, automated document processing, and text analytics platforms that need to identify and classify entities like names, organizations, and locations.
