MMLW-E5-Small
| Property | Value |
|---|---|
| Model Type | Text Encoder |
| Dimensions | 384 |
| Author | sdadas |
| Paper | PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods |
| MTEB Score | 55.84 |
What is mmlw-e5-small?
MMLW-E5-Small is a specialized neural text encoder designed specifically for Polish language processing. It's a distilled model derived from the multilingual E5 checkpoint, trained through knowledge distillation on an extensive dataset of 60 million Polish-English text pairs. The model generates 384-dimensional vector representations of text, making it particularly effective for various natural language processing tasks.
Implementation Details
The model implements a specific prefix-based encoding system where queries must be prefixed with "query: " and passages with "passage: ". It utilizes the sentence-transformers framework and can be easily integrated into existing NLP pipelines.
- Trained using multilingual knowledge distillation with English FlagEmbeddings (BGE) as teacher models
- Achieves NDCG@10 of 47.64 on the Polish Information Retrieval Benchmark
- Optimized for semantic similarity computation and information retrieval tasks
Core Capabilities
- Text embedding generation for Polish language
- Semantic similarity analysis
- Information retrieval
- Text clustering
- Foundation for task-specific fine-tuning
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically optimized for Polish language processing, using a novel distillation approach from multilingual E5 and achieving strong performance on Polish-specific benchmarks. Its compact 384-dimensional representations make it efficient while maintaining high accuracy.
Q: What are the recommended use cases?
The model is ideal for applications requiring semantic text understanding in Polish, including document similarity comparison, information retrieval systems, clustering applications, and as a foundation for specialized NLP tasks through fine-tuning.
