multi-qa-distilbert-cos-v1
| Property | Value |
|---|---|
| Parameter Count | 66.4M |
| Embedding Dimensions | 768 |
| Training Data | 215M QA pairs |
| Model Type | DistilBERT |
| Tensor Type | F32 |
What is multi-qa-distilbert-cos-v1?
multi-qa-distilbert-cos-v1 is a powerful sentence transformer model designed specifically for semantic search applications. Built on the DistilBERT architecture, this model has been trained on an impressive dataset of 215 million question-answer pairs from diverse sources including WikiAnswers, Stack Exchange, and MS MARCO. It maps sentences and paragraphs to a 768-dimensional dense vector space, enabling efficient semantic similarity comparisons.
Implementation Details
The model implements mean pooling and produces normalized embeddings, making it particularly efficient for similarity computations. It can be easily integrated using either the sentence-transformers library or HuggingFace Transformers, with support for both dot-product and cosine-similarity scoring functions.
- Pre-trained on distilbert-base-uncased architecture
- Supports text up to 512 tokens (optimal for ≤250 tokens)
- Trained using MultipleNegativesRankingLoss with cosine-similarity
- Implements efficient mean pooling strategy
Core Capabilities
- Semantic search and retrieval
- Question-answer matching
- Text similarity computation
- Dense vector embeddings generation
- Cross-sentence semantic understanding
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its extensive training on 215M question-answer pairs from 12 diverse datasets, making it particularly robust for semantic search applications. Its efficient architecture balances performance with model size, using only 66.4M parameters.
Q: What are the recommended use cases?
The model excels in semantic search applications, question-answer matching, and document retrieval tasks. It's particularly effective for applications requiring understanding of semantic similarity between text passages up to 250 tokens in length.
