T5-Small-Sinhala-Sumarization-test3

This model is a fine-tuned version of Malmika/T5-Small-Sinhala-Sumarization on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0304
• Rouge1: 0.1355
• Rouge2: 0.0618
• Rougel: 0.1354
• Rougelsum: 0.1356
• Gen Len: 17.8198

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: 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	Gen Len
0.0959	1.0	4333	0.0560	0.1357	0.062	0.1357	0.1358	17.8575
0.0531	2.0	8666	0.0367	0.1355	0.0619	0.1355	0.1357	17.8214
0.0406	3.0	12999	0.0350	0.1355	0.0619	0.1355	0.1357	17.8213
0.0342	4.0	17332	0.0328	0.1355	0.0618	0.1354	0.1356	17.8198
0.0323	5.0	21665	0.0304	0.1355	0.0618	0.1354	0.1356	17.8198

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.17.0
• Tokenizers 0.15.1