sentence-flaubert-base
| Property | Value |
|---|---|
| Parameter Count | 137M |
| Model Type | Sentence Embedding |
| Architecture | FlauBERT Base (Fine-tuned) |
| Author | Lajavaness |
| HuggingFace URL | Link |
What is sentence-flaubert-base?
sentence-flaubert-base is a state-of-the-art French sentence embedding model that leverages the FlauBERT architecture to generate high-quality semantic representations of French text. The model is fine-tuned using Siamese BERT-Networks and Augmented SBERT techniques on the STSB dataset, incorporating advanced pair sampling strategies through CrossEncoder-camembert-large and sentence-camembert-large models.
Implementation Details
The model utilizes a sophisticated architecture combining pre-trained FlauBERT base uncased with Siamese neural networks. It achieves impressive performance metrics, including 85.5% Pearson correlation on the STS-B benchmark, outperforming other French language models across multiple evaluation metrics.
- Fine-tuned using Siamese BERT-Networks
- Incorporates Augmented SBERT methodology
- Optimized with pair sampling strategies
- Achieves superior performance on French STS benchmarks
Core Capabilities
- Generate high-quality French sentence embeddings
- Strong performance on semantic similarity tasks
- Consistent results across multiple benchmark datasets (STS12-16, SICK-fr)
- Easy integration with the sentence-transformers library
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its superior performance on French semantic tasks, achieving the highest scores across multiple benchmarks (87.24% on STS13-fr, 88.00% on STS15-fr) while maintaining consistent performance across different evaluation metrics.
Q: What are the recommended use cases?
This model is ideal for French natural language processing tasks including semantic similarity analysis, text classification, and document comparison. It's particularly well-suited for applications requiring accurate semantic understanding of French text.
