jjae/kobart-summarization-diary

summary2

This model is a fine-tuned version of gogamza/kobart-summarization on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3377

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: 5.6e-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: 300
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss
1.5089	1.23	500	0.3360
0.238	2.47	1000	0.3377
0.1456	3.7	1500	0.3513
0.0848	4.94	2000	0.3753
0.0482	6.17	2500	0.4024

Framework versions

• Transformers 4.39.3
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.15.0