BART_1st_STAGE_SUMMARIZER_v3

This model is a fine-tuned version of facebook/bart-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.0858
• Rouge1: 0.7512
• Rouge2: 0.5341
• Rougel: 0.6975
• Rougelsum: 0.702
• Wer: 0.3693

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: 6
• eval_batch_size: 6
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Wer
No log	0.21	250	1.3647	0.7195	0.4836	0.6589	0.6639	0.4174
1.954	0.42	500	1.2505	0.7301	0.4985	0.6698	0.6744	0.406
1.954	0.63	750	1.2094	0.7341	0.5051	0.6764	0.6799	0.3973
1.3635	0.84	1000	1.1771	0.7387	0.5144	0.6837	0.688	0.3902
1.3635	1.05	1250	1.1862	0.7422	0.5177	0.6853	0.6907	0.3892
1.2246	1.26	1500	1.1514	0.7416	0.5182	0.686	0.6905	0.3869
1.2246	1.48	1750	1.1415	0.7448	0.5219	0.6884	0.693	0.3837
1.1592	1.69	2000	1.1235	0.7459	0.5252	0.691	0.6953	0.38
1.1592	1.9	2250	1.1210	0.7481	0.5284	0.6921	0.697	0.3778
1.1084	2.11	2500	1.1074	0.7487	0.5295	0.6939	0.6987	0.376
1.1084	2.32	2750	1.0985	0.7505	0.5317	0.6961	0.7009	0.3738
1.0452	2.53	3000	1.0907	0.7507	0.5324	0.6962	0.7006	0.3718
1.0452	2.74	3250	1.0884	0.7512	0.5339	0.6973	0.7023	0.3702
1.0253	2.95	3500	1.0858	0.7512	0.5341	0.6975	0.702	0.3693

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2