distilbart-cnn-12-6-finetuned-pubmed

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

• Loss: 1.9895
• Rouge1: 40.0985
• Rouge2: 16.5016
• Rougel: 24.8319
• Rougelsum: 36.0775
• Gen Len: 141.884

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: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
2.1709	1.0	4000	2.0257	38.1012	15.112	23.4064	33.9373	141.9195
1.9495	2.0	8000	1.9593	39.529	16.1693	24.487	35.5238	141.9785
1.756	3.0	12000	1.9488	39.9623	16.5799	24.949	35.9194	141.8855
1.6032	4.0	16000	1.9732	39.672	16.1994	24.5996	35.7021	141.921
1.4817	5.0	20000	1.9895	40.0985	16.5016	24.8319	36.0775	141.884

Framework versions

• Transformers 4.17.0
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.6