all-distilroberta-v1
| Property | Value |
|---|---|
| Parameter Count | 82.1M |
| License | Apache 2.0 |
| Architecture | DistilRoBERTa |
| Training Data | 1B+ sentence pairs |
| Output Dimension | 768 |
What is all-distilroberta-v1?
all-distilroberta-v1 is a powerful sentence embedding model that maps sentences and paragraphs to a 768-dimensional dense vector space. Built on the DistilRoBERTa architecture, this model was fine-tuned on over 1 billion sentence pairs across 21 diverse datasets, making it highly versatile for semantic similarity tasks.
Implementation Details
The model uses a contrastive learning approach during fine-tuning, where it learns to identify true sentence pairs from randomly sampled alternatives. It was trained for 920k steps using a batch size of 512 on TPU v3-8 hardware, with an AdamW optimizer and 2e-5 learning rate.
- Built on pretrained distilroberta-base architecture
- Optimized for sequences up to 128 tokens
- Implements efficient mean pooling strategy
- Supports multiple deep learning frameworks including PyTorch and ONNX
Core Capabilities
- Semantic search and information retrieval
- Text clustering and organization
- Sentence similarity comparison
- Cross-document semantic analysis
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness comes from its extensive training on over 1 billion sentence pairs from diverse sources, including Reddit comments, scientific papers, and question-answer pairs, making it highly versatile for various semantic tasks.
Q: What are the recommended use cases?
The model excels at tasks requiring semantic understanding, including information retrieval, document clustering, and similarity search. It's particularly effective for applications needing to understand the meaning and relationships between sentences or short paragraphs.
