t5-small-Abstractive-Summarizer

This model is a fine-tuned version of t5-small on the multi_news dataset. It achieves the following results on the evaluation set:

• Loss: 2.7737
• Rouge1: 15.7032
• Rouge2: 5.2433
• Rougel: 12.282
• Rougelsum: 14.0946

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.00056
• 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
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
3.118	1.0	113	2.7677	15.1343	4.7712	11.8812	13.386
2.7857	2.0	226	2.7609	15.7641	4.8705	12.0955	13.9779
2.6158	3.0	339	2.7494	15.1515	4.4523	11.7147	13.4181
2.4962	4.0	452	2.7743	15.344	5.1073	12.1574	13.7917
2.4304	5.0	565	2.7737	15.7032	5.2433	12.282	14.0946

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1