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Bert Mini2bert Mini Finetuned Cnn Daily Mail Summarization

Developed by mrm8488
This is an encoder-decoder model based on the BERT-mini architecture, specifically fine-tuned for the CNN/Dailymail dataset for text summarization tasks.
Downloads 140
Release Time : 3/2/2022

Model Overview

The model adopts a warm-started BERT2BERT architecture (mini version), fine-tuned on the CNN/Dailymail summarization dataset, capable of generating high-quality text summaries.

Model Features

Efficient Small Model
Based on the BERT-mini architecture, it provides good summarization capabilities while maintaining a small model size.
Domain-Specific Fine-Tuning
Specifically optimized for the CNN/Dailymail news dataset, making it suitable for news text summarization.
Warm-Start Training
Uses pre-trained models for warm-start, improving training efficiency and model performance.

Model Capabilities

Text Summarization Generation
News Content Condensation
Long Text Compression

Use Cases

News Media
Automatic News Summarization
Generates concise summaries for long news articles
ROUGE-2 score of 16.51 on the CNN/Dailymail test set
Content Analysis
Document Content Extraction
Extracts key information from long documents
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