Distilbart-xsum-1-1 Open-source Text Summarization Model - Free Deployment, Efficient and Concise Content Extraction

Distilbart Xsum 1 1

Developed by sshleifer

DistilBART is a distilled version of the BART model, optimized for text summarization tasks, significantly reducing model size and inference time while maintaining high performance.

Text Generation EnglishOpen Source License:Apache-2.0 #Text Summarization #Efficient Inference #News Summarization

Downloads 2,198

Release Time : 3/2/2022

Model Overview

A distilled model based on the BART architecture, primarily used for generative text summarization tasks, supporting English text processing. Compresses the original model through knowledge distillation techniques for more efficient inference.

Model Features

Efficient Inference

Compared to the original BART-large model, inference speed is increased by 1.68 times while maintaining over 90% of Rouge scores.

Multi-version Configuration

Provides sub-versions with different parameter scales (12-1/6-6/12-3, etc.) to balance performance and efficiency requirements.

Dual Dataset Adaptation

Optimized for CNN/DailyMail and XSum datasets respectively, catering to different summarization style needs.

Model Capabilities

Generative Text Summarization

Long Text Compression

Key Information Extraction

Use Cases

News Media

News Brief Generation

Automatically compresses long news reports into concise summaries

Achieves 22.12 Rouge-2 score on the XSum dataset

Content Analysis

Document Summary Generation

Extracts core content from long documents to form executive summaries

Achieves 21.26 Rouge-2 score on the CNN/DailyMail dataset

Model Name	MM Params	Inference Time (MS)	Speedup	Rouge 2	Rouge-L
distilbart-xsum-12-1	222	90	2.54	18.31	33.37
distilbart-xsum-6-6	230	132	1.73	20.92	35.73
distilbart-xsum-12-3	255	106	2.16	21.37	36.39
distilbart-xsum-9-6	268	136	1.68	21.72	36.61
bart-large-xsum (baseline)	406	229	1	21.85	36.50
distilbart-xsum-12-6	306	137	1.68	22.12	36.99
bart-large-cnn (baseline)	406	381	1	21.06	30.63
distilbart-12-3-cnn	255	214	1.78	20.57	30.00
distilbart-12-6-cnn	306	307	1.24	21.26	30.59
distilbart-6-6-cnn	230	182	2.09	20.17	29.70

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Distilbart Xsum 1 1

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 DistilBART Model

🚀 Quick Start

💻 Usage Examples

Basic Usage

📚 Documentation

Metrics for DistilBART models

📄 License