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Roberta2roberta L 24 Cnn Daily Mail

Developed by google
An encoder-decoder model initialized with RoBERTa-Large, specifically designed for summarization tasks and fine-tuned on the CNN/DailyMail dataset.
Downloads 128
Release Time : 3/2/2022

Model Overview

This model adopts an encoder-decoder architecture, initialized with the roberta-large checkpoint, and is suitable for text summarization tasks.

Model Features

RoBERTa-Large Initialization
Both the encoder and decoder are initialized from the high-performance roberta-large checkpoint.
Specialized Summarization
Fine-tuned on the CNN/DailyMail dataset, optimized for long-text summarization.
Encoder-Decoder Architecture
Utilizes a standard encoder-decoder structure, ideal for sequence-to-sequence tasks.

Model Capabilities

Text Summarization
Long Text Comprehension
Key Information Extraction

Use Cases

News Media
News Article Summarization
Automatically generate summaries of the core content of news articles.
Produces concise and accurate news highlights.
Content Analysis
Document Key Information Extraction
Extract key information from long documents to generate summaries.
Helps quickly understand the main content of documents.
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