B

Bleurt Tiny 512

Developed by Elron
BLEURT-tiny-512 is a text quality evaluation model developed by Google Research, based on the BERT architecture implemented in PyTorch, used for assessing the quality of natural language generation tasks.
Downloads 291.96k
Release Time : 3/2/2022

Model Overview

This model is a lightweight implementation of the original BLEURT model from the ACL paper, primarily used for text classification and natural language generation quality evaluation tasks.

Model Features

Lightweight Implementation
As a tiny version of BLEURT, it retains core functionalities while being more lightweight.
BERT-based Architecture
Utilizes BERT's powerful semantic understanding capabilities for text quality evaluation.
PyTorch Implementation
Provides a PyTorch framework version for easy integration and use.

Model Capabilities

Text Quality Evaluation
Natural Language Generation Scoring
Text Classification

Use Cases

Natural Language Processing
Machine Translation Quality Evaluation
Evaluates the quality difference between machine translation output and reference translations.
Provides automated scoring with high correlation to human evaluation.
Text Summarization Quality Evaluation
Scores the quality of automatically generated text summaries.
Effectively distinguishes between different quality summary outputs.
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
© 2025AIbase