text-summarization-evaluation-model

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

• Loss: 2.4100
• Rouge1: 0.1909
• Rouge2: 0.0934
• Rougel: 0.1617
• Rougelsum: 0.1619
• Gen Len: 19.0

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: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
No log	1.0	62	2.4775	0.1556	0.0622	0.1297	0.1301	19.0
No log	2.0	124	2.4374	0.1822	0.0868	0.1534	0.1537	19.0
No log	3.0	186	2.4164	0.1888	0.0922	0.16	0.1602	19.0
No log	4.0	248	2.4100	0.1909	0.0934	0.1617	0.1619	19.0

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0