ner-french
| Property | Value |
|---|---|
| Downloads | 352,546 |
| Framework | PyTorch with Flair |
| Dataset | WikiNER French |
| Performance | 90.61% F1-Score |
What is ner-french?
ner-french is a specialized Named Entity Recognition model designed specifically for French language text processing. Developed using the Flair framework, it represents a state-of-the-art approach to identifying and classifying named entities in French text. The model can recognize four distinct entity types: Person (PER), Location (LOC), Organization (ORG), and Miscellaneous (MISC).
Implementation Details
The model employs a sophisticated architecture combining Flair embeddings with an LSTM-CRF (Long Short-Term Memory - Conditional Random Field) sequence labeling approach. It utilizes a stacked embedding strategy that includes French GloVe embeddings and bidirectional Flair embeddings (forward and backward) to capture rich contextual information.
- Utilizes stacked embeddings: French GloVe + Flair forward/backward
- LSTM hidden size of 256 units
- Trained on WikiNER French dataset
- Implements sequence tagging with CRF layer
Core Capabilities
- Accurate identification of person names with contextual understanding
- Precise detection of geographical locations
- Recognition of organization names in French text
- Classification of miscellaneous named entities
- Handles complex French linguistic patterns
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its combination of Flair's contextual string embeddings with traditional word embeddings, specifically optimized for French language processing. The high F1-score of 90.61% on WikiNER demonstrates its exceptional performance in French NER tasks.
Q: What are the recommended use cases?
The model is ideal for applications requiring French text analysis, including information extraction, document processing, and content categorization. It's particularly useful for tasks like automated document analysis, news article processing, and legal document parsing where entity recognition is crucial.
