cnn_xsum_samsum_model

This model is a fine-tuned version of lidiya/bart-large-xsum-samsum on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.6585
• Rouge1: 0.4194
• Rouge2: 0.1959
• Rougel: 0.2948
• Rougelsum: 0.3902
• Gen Len: 60.8916

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.6501	1.0	836	1.6017	0.4143	0.194	0.2912	0.3845	60.7718
1.3162	2.0	1672	1.5954	0.4113	0.1908	0.2891	0.3819	61.3206
1.1452	3.0	2508	1.5853	0.4196	0.1964	0.2945	0.3899	60.928
1.012	4.0	3344	1.6293	0.4201	0.1967	0.2952	0.3911	60.7965
0.9368	5.0	4180	1.6585	0.4194	0.1959	0.2948	0.3902	60.8916

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2