plain-bart-on-presummarized-2-clusters-wcep

This model is a fine-tuned version of sshleifer/distilbart-cnn-6-6 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.0775
• Rouge1: 36.3774
• Rouge2: 15.2074
• Rougel: 25.7706
• Rougelsum: 29.2593
• Gen Len: 67.6608

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: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
2.2178	1.0	510	2.0873	36.3079	15.0162	25.5837	29.129	67.8461
1.8901	2.0	1020	2.0696	36.0914	15.0005	25.6729	29.2956	68.3451
1.7267	3.0	1530	2.0775	36.3774	15.2074	25.7706	29.2593	67.6608

Framework versions

• Transformers 4.39.3
• Pytorch 2.1.2
• Datasets 2.18.0
• Tokenizers 0.15.2