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Bleurt Base 512
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Bleurt Base 512

Developed by Elron
BLEURT is a pre-trained model for evaluating text generation quality, developed by Google Research.
Text Embedding
Transformers
#Text Generation Evaluation#Multi-reference Scoring#Pre-trained Metrics
Downloads 1,084
Release Time : 3/2/2022

Model Overview

BLEURT is a BERT-based sequence classification model specifically designed to assess the quality of machine-generated text. It scores by learning the similarity between reference texts and candidate texts.

Model Features

Robust Evaluation Metric
Provides more robust text generation quality evaluation by learning the similarity between reference texts and candidate texts.
BERT-based Architecture
Leverages BERT's powerful representation capabilities to capture deep semantic features of text.
Pre-trained Model
Offers an out-of-the-box pre-trained model that can be used without additional training.

Model Capabilities

Text Quality Evaluation
Machine Translation Evaluation
Text Summarization Evaluation

Use Cases

Natural Language Processing
Machine Translation Quality Evaluation
Evaluates the similarity between machine-translated text and reference text.
Provides a score between 0-1, where higher scores indicate better quality.
Text Summarization Quality Evaluation
Evaluates the similarity between automatically generated summaries and reference summaries.
Provides a score between 0-1, where higher scores indicate better quality.
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