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Tct Colbert V2 Hnp Msmarco
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Tct Colbert V2 Hnp Msmarco

Developed by castorini
TCT-ColBERT-V2 is a dense retrieval model based on the tightly-coupled teacher mechanism and in-batch negative sample knowledge distillation, designed for efficient text retrieval.
Text Embedding
Transformers
#Dense Retrieval#Knowledge Distillation#In-batch Negative Samples
Downloads 1,382
Release Time : 3/2/2022

Model Overview

This model optimizes dense retrieval performance through knowledge distillation techniques, combining the tightly-coupled teacher mechanism and in-batch negative sample strategy to significantly improve retrieval efficiency and accuracy.

Model Features

Tightly-coupled Teacher Mechanism
Achieves more efficient knowledge distillation through tight coupling between the teacher model and student model.
In-batch Negative Sample Strategy
Optimizes the training process using in-batch negative samples to enhance the model's ability to distinguish negative samples.
Efficient Retrieval
Significantly improves retrieval efficiency while maintaining high retrieval accuracy.

Model Capabilities

Text Retrieval
Semantic Matching
Knowledge Distillation

Use Cases

Information Retrieval
Document Retrieval
Quickly retrieves relevant documents from large-scale document libraries.
High precision and recall rates
Question Answering System
Used for candidate answer retrieval in question answering systems.
Improves the response speed and accuracy of question answering systems
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