bart-base-summarization-medical_on_cnn-44

This model is a fine-tuned version of facebook/bart-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 3.3974
• Rouge1: 0.2518
• Rouge2: 0.0939
• Rougel: 0.1991
• Rougelsum: 0.2234
• Gen Len: 18.193

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: 3e-05
• train_batch_size: 4
• eval_batch_size: 1
• seed: 44
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 6
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
2.703	1.0	1250	3.3758	0.2513	0.0906	0.197	0.222	18.892
2.6143	2.0	2500	3.3837	0.2497	0.0914	0.1956	0.2202	18.433
2.5745	3.0	3750	3.3914	0.2487	0.0908	0.195	0.2188	18.475
2.5622	4.0	5000	3.3947	0.2481	0.0909	0.1957	0.2192	18.307
2.5308	5.0	6250	3.4010	0.2512	0.0935	0.1994	0.2232	18.139
2.5395	6.0	7500	3.3974	0.2518	0.0939	0.1991	0.2234	18.193

Framework versions

• PEFT 0.12.0
• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1