my_awesome_billsum_model

This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the dialogstudio dataset. It achieves the following results on the evaluation set:

• Loss: 1.9811
• Rouge1: 0.4187
• Rouge2: 0.1911
• Rougel: 0.3373
• Rougelsum: 0.338
• Gen Len: 65.1636

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	55	2.0591	0.4232	0.1899	0.3412	0.342	64.8545
No log	2.0	110	1.9802	0.4125	0.19	0.3329	0.3334	66.7545
No log	3.0	165	1.9671	0.4172	0.1927	0.3348	0.3357	65.3545
No log	4.0	220	1.9811	0.4187	0.1911	0.3373	0.338	65.1636

Framework versions

• Transformers 4.41.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1