T5-ANCE Open-Source Information Retrieval Model - Optimized Based on High-Quality Datasets to Efficiently Complete Retrieval Tasks

T5 Ance

Developed by OpenMatch

T5-ANCE is a dense retrieval model trained on the MS MARCO passage dataset, optimized with the T5 architecture and ANCE training process, suitable for information retrieval tasks.

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Transformers

Open Source License:MIT #Dense Retrieval #MS MARCO Optimization #Large Batch Training

Downloads 893

Release Time : 10/26/2022

Model Overview

T5-ANCE is a dense retrieval model primarily used for information retrieval tasks, capable of efficiently matching queries with relevant documents.

Model Features

Large Batch Training

Trained with a global batch size of 256, improving the model's training efficiency and performance.

Optimized Query and Document Processing

Uses query templates and document templates (including titles and body text) for preprocessing to enhance retrieval effectiveness.

Model Capabilities

Dense Retrieval

Query-Document Matching

Use Cases

Information Retrieval

Search Engine

Used for query-document matching in search engines to improve the relevance of search results.

Achieved an MRR@10 of 0.3570 on the MS MARCO passage dataset (dev set).

Dataset	Metric	Result
MS MARCO Passage (dev)	MRR@10	0.3570

Property	Details
Global batch size	256
Learning rate	5e-6
Maximum length of query	32
Maximum length of document	128
Template for query	`<text>`
Template for document	`Title: <title> Text: <text>`

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T5 Ance

Model Overview

Model Features

Model Capabilities

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📚 Documentation

Dataset

Evaluation Result

Important Hyper-parameters

Paper

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