camembert-keyword-extractor
| Property | Value |
|---|---|
| License | MIT |
| Framework | PyTorch 1.11.0 |
| Language | French |
| Best F1 Score | 0.6859 |
What is camembert-keyword-extractor?
The camembert-keyword-extractor is a specialized natural language processing model fine-tuned on the CamemBERT base architecture for French keyword extraction. It demonstrates strong performance with 93.46% accuracy and 68.59% F1 score, making it particularly effective for French text analysis tasks.
Implementation Details
The model utilizes the Adam optimizer with carefully tuned hyperparameters (betas=0.9,0.999, epsilon=1e-08) and implements a linear learning rate scheduler. Training was conducted over 8 epochs with a learning rate of 2e-05 and batch sizes of 16 for both training and evaluation.
- Native AMP mixed precision training
- Seed value: 42 for reproducibility
- Achieved validation loss: 0.2199
- Precision: 0.6743
Core Capabilities
- French text keyword extraction
- Token classification
- Optimized for production deployment with inference endpoints
- Compatible with Transformers pipeline
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in French language keyword extraction, built on the robust CamemBERT architecture with extensive fine-tuning and optimization for production use. Its high accuracy and F1 score make it particularly reliable for French text analysis.
Q: What are the recommended use cases?
The model is ideal for French text analysis tasks including keyword extraction from articles, documents, and web content. It's particularly suited for applications requiring automated content tagging, document classification, and text summarization in French.
