bart-large-summarization-medical_on_cnn-43

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

• Loss: 3.0298
• Rouge1: 0.2443
• Rouge2: 0.0871
• Rougel: 0.193
• Rougelsum: 0.2171
• Gen Len: 18.859

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: 43
• 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
2.2146	1.0	1250	3.0347	0.2365	0.083	0.1868	0.2101	19.329
2.1322	2.0	2500	3.0354	0.2419	0.0862	0.1911	0.2142	19.0
2.0892	3.0	3750	3.0422	0.2411	0.0851	0.1903	0.2134	18.943
2.0772	4.0	5000	3.0387	0.2423	0.0857	0.1911	0.2145	18.869
2.0742	5.0	6250	3.0307	0.2448	0.0868	0.193	0.2171	18.828
2.0673	6.0	7500	3.0298	0.2443	0.0871	0.193	0.2171	18.859

Framework versions

• PEFT 0.11.1
• Transformers 4.42.4
• Pytorch 2.3.1+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1