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LA1512/
fine-tuned-distilbart-xsum-12-3-news-summary

Name: fine-tuned-distilbart-xsum-12-3-news-summary
Availability: InStock

generated_from_trainer

sshleifer/distilbart-xsum-12-3

finetune

apache-2.0

endpoints_compatible

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results

This model is a fine-tuned version of sshleifer/distilbart-xsum-12-3 on the News-summary-Kaggle dataset. It achieves the following results on the evaluation set:

Loss: 3.1426
Rouge1: 51.2701
Rouge2: 28.3575
Rougel: 37.9263
Rougelsum: 45.8934
Gen Len: 75.777

Model description

This model use pre-trained model: sshleifer/distilbart-xsum-12-3 fined tuned on the datasets: News-summary-Kaggle. Our aims is to build model can summerize news efficiently.

Intended uses & limitations

More information needed

Training and evaluation data

News-summary. Link: https://www.kaggle.com/datasets/sunnysai12345/news-summary

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 5
label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
3.4812	1.0	425	3.3209	47.7226	26.3282	35.5063	42.5426	66.523
3.2269	2.0	850	3.1838	50.4271	27.7047	37.2638	45.1897	77.115
2.9504	3.0	1275	3.1401	50.6362	28.2773	37.6	45.4901	74.992
2.8014	4.0	1700	3.1346	51.2942	28.4684	38.0877	46.0386	74.299
2.71	5.0	2125	3.1426	51.2701	28.3575	37.9263	45.8934	75.777

Framework versions

Transformers 4.38.2
Pytorch 2.1.2
Datasets 2.1.0
Tokenizers 0.15.2

LA1512/fine-tuned-distilbart-xsum-12-3-news-summary