Barthez-Orange-FT Open-Source Text Summarization Model - Free Deployment, Excellently Complete French Text Summarization Tasks

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Barthez Orange Ft

Developed by amasi

A fine-tuned text summarization model based on barthez-orangesum-abstract, excelling in French text summarization tasks

Text Generation

Transformers

Open Source License:Apache-2.0 #French Abstract Generation #High Rouge Score #BART Fine-tuning

Downloads 26

Release Time : 5/28/2024

Model Overview

This model is a BART architecture model specifically optimized for French text summarization tasks, with enhanced summarization capabilities in specific domains through fine-tuning

Model Features

Optimized French Text Summarization

Summarization capabilities specifically optimized for French text

Efficient Fine-tuning

Efficient fine-tuning based on pre-trained models to improve performance on specific tasks

High-Quality Summaries

Excellent performance on ROUGE metrics, capable of generating high-quality text summaries

Model Capabilities

French Text Understanding

Automatic Summary Generation

Text Compression

Use Cases

News Media

News Article Summarization

Automatically generate key summaries of news articles

ROUGE-1 score of 0.6719, indicating high summary quality

Academic Research

Thesis Abstract Generation

Generate concise summaries for French academic papers

🚀 barthez-orange-ft

This model is a fine - tuned version of moussaKam/barthez-orangesum-abstract, aiming to achieve better performance on specific tasks. It provides evaluation metrics such as loss and ROUGE scores.

📚 Documentation

This model is a fine-tuned version of moussaKam/barthez-orangesum-abstract on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1689
Rouge1: 0.6719
Rouge2: 0.6536
Rougel: 0.6719
Rougelsum: 0.6722
Gen Len: 20.0

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	31	4.6662	0.6719	0.6535	0.6718	0.6721	20.0
No log	1.99	62	0.6939	0.6718	0.6535	0.6718	0.6721	20.0
No log	2.99	93	0.2939	0.6718	0.6535	0.6718	0.6721	20.0
No log	3.98	124	0.2089	0.6719	0.6535	0.6718	0.6721	20.0
No log	4.98	155	0.1880	0.6719	0.6535	0.6718	0.6721	20.0
No log	5.98	186	0.1795	0.6719	0.6535	0.6718	0.6721	20.0
No log	6.97	217	0.1752	0.6719	0.6535	0.6718	0.6721	20.0
No log	8.0	249	0.1732	0.6719	0.6535	0.6718	0.6721	20.0
No log	9.0	280	0.1716	0.6719	0.6536	0.6719	0.6722	20.0
No log	9.99	311	0.1707	0.6719	0.6536	0.6719	0.6722	20.0
No log	10.99	342	0.1704	0.6719	0.6536	0.6719	0.6722	20.0
No log	11.98	373	0.1696	0.6719	0.6536	0.6719	0.6722	20.0
No log	12.98	404	0.1698	0.6719	0.6536	0.6719	0.6722	20.0
No log	13.98	435	0.1695	0.6719	0.6536	0.6719	0.6722	20.0
No log	14.97	466	0.1693	0.6719	0.6536	0.6719	0.6722	20.0
No log	16.0	498	0.1691	0.6719	0.6536	0.6719	0.6722	20.0
0.9743	17.0	529	0.1691	0.6719	0.6536	0.6719	0.6722	20.0
0.9743	17.99	560	0.1690	0.6719	0.6536	0.6719	0.6722	20.0
0.9743	18.99	591	0.1689	0.6719	0.6536	0.6719	0.6722	20.0
0.9743	19.92	620	0.1689	0.6719	0.6536	0.6719	0.6722	20.0

Framework versions

Transformers 4.31.0
Pytorch 2.1.2
Datasets 2.18.0
Tokenizers 0.13.3

📄 License

This model is licensed under the Apache-2.0 license.

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