answerai-colbert-small-v1
| Property | Value |
|---|---|
| Parameter Count | 33.4M |
| License | Apache-2.0 |
| Paper | JaColBERTv2.5 Paper |
| Tensor Type | F32 |
| Downloads | 3.4M+ |
What is answerai-colbert-small-v1?
answerai-colbert-small-v1 is a groundbreaking proof-of-concept model developed by Answer.AI that demonstrates exceptional performance in passage retrieval tasks. Despite its compact size of just 33 million parameters, it outperforms not only similarly-sized models but also larger models like e5-large-v2 and bge-base-en-v1.5. The model implements the innovative JaColBERTv2.5 training recipe with additional optimizations.
Implementation Details
The model is designed for maximum compatibility and can be implemented through multiple frameworks including RAGatouille, Stanford ColBERT library, and the rerankers library. It uses a multi-vector architecture that enables superior retrieval performance while maintaining computational efficiency.
- Compatible with all recent ColBERT implementations
- Supports both retrieval and re-ranking tasks
- Optimized for 512 token document length
- Implements 2-bit precision indexing capability
Core Capabilities
- Achieves 53.79% average performance on BEIR benchmark
- Excels in specific tasks like FEVER (90.96%) and TRECCOVID (84.59%)
- Performs remarkably well in question-answering tasks (HotpotQA: 76.11%)
- Functions as both retriever and re-ranker
Frequently Asked Questions
Q: What makes this model unique?
The model achieves state-of-the-art performance with just 33M parameters, making it extremely efficient while maintaining high accuracy. It's particularly notable for outperforming models 3.3x its size.
Q: What are the recommended use cases?
The model excels in passage retrieval, document search, and re-ranking tasks. It's particularly effective for question-answering systems, fact verification, and general information retrieval applications.
