News Summarizer T5 Open-Source News Summarization Model - Free Deployment for Quick Generation of Concise News Summaries

News Summarizer T5

Developed by SurAyush

A news summarization generation model fine-tuned based on google-t5/t5-small, supporting concise summarization of news texts.

Text Generation

Transformers

Open Source License:Apache-2.0 #T5 fine-tuned model #News summarization generation #ROUGE evaluation

Downloads 62

Release Time : 3/31/2025

Model Overview

This model is a text generation model fine-tuned based on the T5-small architecture, specifically designed for news summarization tasks. Through sequence-to-sequence transformation, it can compress lengthy news texts into concise summaries.

Model Features

Efficient summarization generation

Capable of quickly processing news texts and generating concise, coherent summaries

Based on T5 architecture

Utilizes Google's T5-small model architecture, offering strong text comprehension and generation capabilities

Lightweight model

The small version has fewer parameters, making it suitable for deployment in resource-limited environments

Model Capabilities

Text summarization generation

Sequence-to-sequence transformation

News content compression

Use Cases

News media

Automatic news summarization

Generates concise summaries for lengthy news reports, enabling readers to quickly grasp the main content

Rouge1 score reached 19.88, effectively capturing key information

Content aggregation

News aggregation platform

Automatically generates news preview summaries in news aggregation applications

🚀 news-summarizer-t5

This model is a fine - tuned text summarization model based on the T5 architecture, offering efficient and accurate news summarization capabilities.

🚀 Quick Start

This model is a fine - tuned version of [google - t5/t5 - small](https://huggingface.co/google - t5/t5 - small) on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6177
Model Preparation Time: 0.0049
Rouge1: 19.8849
Rouge2: 17.9939
Rougel: 19.5328
Rougelsum: 19.5918

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon = 1e - 08 and optimizer_args = No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 8
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Rouge1	Rouge2	Rougel	Rougelsum
0.8689	1.0	251	0.6581	0.0049	18.8745	16.2314	18.1991	18.3287
0.6629	2.0	502	0.6385	0.0049	19.3705	17.1277	18.8685	18.9594
0.6114	3.0	753	0.6294	0.0049	19.3951	17.2113	18.9315	18.9848
0.571	4.0	1004	0.6197	0.0049	19.8684	17.8234	19.4646	19.5401
0.5451	5.0	1255	0.6193	0.0049	19.8981	17.9851	19.5083	19.5177
0.5194	6.0	1506	0.6203	0.0049	19.8675	17.9521	19.5434	19.6046
0.4894	7.0	1757	0.6166	0.0049	19.8622	17.9616	19.4791	19.5669
0.4872	8.0	2008	0.6177	0.0049	19.8849	17.9939	19.5328	19.5918

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

📄 License

The model is licensed under the Apache 2.0 license.

📦 Metadata

Property	Details
Library Name	transformers
Base Model	google - t5/t5 - small
Tags	summarization, generated_from_trainer
Metrics	rouge
Model Name	news - summarizer - t5

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