vietnamese-sbert
| Property | Value |
|---|---|
| Author | keepitreal |
| Downloads | 18,434 |
| Vector Dimension | 768 |
| Framework | PyTorch + Transformers |
What is vietnamese-sbert?
vietnamese-sbert is a specialized sentence transformer model designed specifically for Vietnamese language processing. Built on the SBERT (Sentence-BERT) architecture, it transforms Vietnamese text into dense 768-dimensional vector representations, enabling powerful semantic search and similarity analysis capabilities.
Implementation Details
The model is implemented using the sentence-transformers framework and is based on RoBERTa architecture. It was trained using CosineSimilarityLoss with AdamW optimizer, featuring a learning rate of 2e-05 and 4 training epochs. The model implements mean pooling strategy for generating sentence embeddings.
- Maximum sequence length: 256 tokens
- Warmup steps: 144
- Weight decay: 0.01
- Batch size: 16
Core Capabilities
- Semantic similarity computation for Vietnamese text
- Dense vector representation generation
- Text clustering and classification
- Semantic search functionality
- Cross-lingual document matching
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Vietnamese language processing, offering state-of-the-art sentence embedding capabilities while being fully compatible with the sentence-transformers ecosystem. Its architecture is carefully tuned for Vietnamese semantic understanding.
Q: What are the recommended use cases?
The model excels in applications requiring semantic understanding of Vietnamese text, including document similarity analysis, semantic search engines, content recommendation systems, and automated text clustering. It's particularly useful for businesses and researchers working with Vietnamese language content.
