t5-abs-1609-1545-lr-0.0001-bs-10-maxep-20

This model is a fine-tuned version of google-t5/t5-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.9472
• Rouge/rouge1: 0.4676
• Rouge/rouge2: 0.2222
• Rouge/rougel: 0.4004
• Rouge/rougelsum: 0.4024
• Bertscore/bertscore-precision: 0.8963
• Bertscore/bertscore-recall: 0.8971
• Bertscore/bertscore-f1: 0.8965
• Meteor: 0.4301
• Gen Len: 40.8455

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: 0.0001
• train_batch_size: 10
• eval_batch_size: 10
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 20
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge/rouge1	Rouge/rouge2	Rouge/rougel	Rouge/rougelsum	Bertscore/bertscore-precision	Bertscore/bertscore-recall	Bertscore/bertscore-f1	Meteor	Gen Len
2.79	0.9885	43	2.0712	0.4222	0.1746	0.3527	0.3532	0.8934	0.886	0.8895	0.3645	37.0818
1.9564	2.0	87	1.7989	0.445	0.202	0.3739	0.3753	0.8971	0.8893	0.893	0.3978	36.1545
1.7565	2.9885	130	1.7408	0.4648	0.2233	0.3974	0.3992	0.8987	0.8919	0.8952	0.416	36.3818
1.5584	4.0	174	1.7210	0.4529	0.2075	0.3814	0.3835	0.8969	0.8919	0.8942	0.4083	37.7545
1.4732	4.9885	217	1.7176	0.4639	0.2163	0.3949	0.3968	0.8978	0.8942	0.8958	0.4189	38.0727
1.3447	6.0	261	1.7308	0.4541	0.208	0.3856	0.3877	0.8962	0.8934	0.8947	0.4111	38.8636
1.2905	6.9885	304	1.7456	0.4584	0.2067	0.3851	0.3868	0.8975	0.8934	0.8953	0.4114	37.0909
1.1838	8.0	348	1.7674	0.4636	0.2164	0.3945	0.3961	0.8951	0.8969	0.8958	0.4287	41.3545
1.1479	8.9885	391	1.7794	0.4721	0.2233	0.4014	0.4037	0.8957	0.8964	0.8959	0.4378	41.2545
1.067	10.0	435	1.8149	0.4567	0.2127	0.3968	0.3984	0.895	0.8935	0.894	0.4188	38.9182
1.0456	10.9885	478	1.8434	0.4585	0.208	0.3894	0.3913	0.8964	0.8936	0.8948	0.4136	37.8364
0.9792	12.0	522	1.8381	0.466	0.2163	0.3976	0.3996	0.8962	0.8962	0.896	0.4272	40.3455
0.9618	12.9885	565	1.8834	0.4702	0.2214	0.3996	0.4023	0.8949	0.8978	0.8962	0.441	42.6
0.9077	14.0	609	1.8886	0.4664	0.2221	0.4001	0.4014	0.8958	0.8969	0.8962	0.433	41.7364
0.9053	14.9885	652	1.9082	0.4687	0.2231	0.4016	0.4043	0.8967	0.898	0.8972	0.4341	41.7182
0.8627	16.0	696	1.9271	0.4564	0.2097	0.3858	0.3869	0.8939	0.8957	0.8946	0.4231	41.7545
0.866	16.9885	739	1.9276	0.4615	0.2129	0.3936	0.3955	0.8945	0.8971	0.8957	0.4273	42.0091
0.8359	18.0	783	1.9376	0.4644	0.2186	0.3995	0.4012	0.8947	0.8966	0.8955	0.4309	41.7091
0.8412	18.9885	826	1.9467	0.4691	0.2222	0.4018	0.4035	0.8959	0.898	0.8968	0.4327	41.6909
0.8218	19.7701	860	1.9472	0.4676	0.2222	0.4004	0.4024	0.8963	0.8971	0.8965	0.4301	40.8455

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• Datasets 2.21.0
• Tokenizers 0.19.1