paraphrase-albert-small-v2
| Property | Value |
|---|---|
| Parameter Count | 11.7M |
| Output Dimensions | 768 |
| License | Apache 2.0 |
| Framework Support | PyTorch, TensorFlow, ONNX, Rust |
| Paper | Sentence-BERT Paper |
What is paraphrase-albert-small-v2?
paraphrase-albert-small-v2 is a lightweight sentence transformer model based on the ALBERT architecture, designed specifically for generating semantic sentence embeddings. It converts sentences and paragraphs into 768-dimensional dense vector representations, making it ideal for tasks like semantic search, clustering, and similarity comparison.
Implementation Details
The model utilizes a two-component architecture combining an ALBERT transformer with a pooling layer. It processes text with a maximum sequence length of 100 tokens and applies mean pooling to generate the final embeddings. The model has been trained on 13 diverse datasets, including StackExchange, MS MARCO, and SNLI.
- Efficient architecture with only 11.7M parameters
- Mean pooling strategy for consistent embeddings
- Compatible with multiple deep learning frameworks
- Pre-trained on diverse text sources
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Paraphrase detection
- Text clustering and organization
- Information retrieval tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture that maintains strong performance while using significantly fewer parameters than larger models. It's specifically optimized for paraphrase detection and semantic similarity tasks, making it ideal for production environments where resource efficiency is crucial.
Q: What are the recommended use cases?
The model is best suited for applications requiring semantic text matching, such as document similarity search, content recommendation systems, duplicate detection, and automated paraphrase identification. It's particularly valuable in scenarios where computational efficiency is important.
