sbertimbau-large-nli-sts
| Property | Value |
|---|---|
| Embedding Dimension | 1024 |
| Model Type | Sentence Transformer |
| Training Epochs | 4 |
| Batch Size | 16 |
| Learning Rate | 2e-05 |
What is sbertimbau-large-nli-sts?
sbertimbau-large-nli-sts is a sophisticated sentence transformer model designed for semantic similarity tasks. It transforms sentences and paragraphs into 1024-dimensional dense vector representations, making it particularly effective for applications like clustering and semantic search. The model utilizes the BERT architecture and has been fine-tuned using carefully selected parameters and optimization techniques.
Implementation Details
The model implements a two-stage architecture combining a transformer module with a pooling layer. It processes input text with a maximum sequence length of 64 tokens and employs mean pooling for generating sentence embeddings. The training process utilized the AdamW optimizer with a learning rate of 2e-05 and incorporated a warmup linear scheduler over 143 steps.
- Utilizes CosineSimilarityLoss for training
- Implements mean pooling strategy for embedding generation
- Supports both sentence-transformers and HuggingFace Transformers implementations
- Features automatic batch processing and attention masking
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Clustering support
- Cross-lingual text comparison
- Efficient batch processing of multiple sentences
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its large 1024-dimensional embedding space and optimized architecture combining BERT with efficient pooling strategies. It's specifically designed for semantic similarity tasks and offers flexible implementation options through both sentence-transformers and HuggingFace frameworks.
Q: What are the recommended use cases?
The model is ideal for applications requiring semantic similarity matching, document clustering, information retrieval, and semantic search functionality. It's particularly effective for tasks requiring nuanced understanding of sentence relationships and semantic meaning.
