msmarco-t5-base-v1
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Base Model | google/t5-v1_1-base |
| Research Paper | doc2query Paper |
| Training Data | MS MARCO Passage-Ranking dataset |
What is msmarco-t5-base-v1?
msmarco-t5-base-v1 is a specialized T5-based model designed for document expansion and query generation. Built on the doc2query architecture, this model excels at generating relevant search queries from document passages, effectively bridging the lexical gap in information retrieval systems.
Implementation Details
The model was fine-tuned from google/t5-v1_1-base for 31,000 training steps (approximately 4 epochs) using 500,000 training pairs from MS MARCO. It processes input text up to 320 word pieces and generates outputs up to 64 word pieces in length.
- Built on T5 architecture with doc2query optimization
- Supports both document expansion and training data generation
- Implements non-deterministic query generation
- Trained on MS MARCO Passage-Ranking dataset
Core Capabilities
- Document Expansion: Generates 20-40 queries per paragraph for enhanced BM25 indexing
- Query Generation: Creates relevant search queries from text passages
- Training Data Generation: Produces (query, text) pairs for embedding model training
- Lexical Gap Bridging: Generates synonyms and important terms to improve search relevance
Frequently Asked Questions
Q: What makes this model unique?
This model's unique strength lies in its ability to generate multiple relevant queries from text passages, making it particularly valuable for improving search engine performance and generating training data for other NLP models.
Q: What are the recommended use cases?
The model is ideal for enhancing search engine capabilities through document expansion, generating training data for embedding models, and improving information retrieval systems. It's particularly effective when integrated with BM25-based search engines like Elasticsearch or OpenSearch.
