t5-abs-2309-1054-lr-0.001-bs-2-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: nan
• Rouge/rouge1: 0.0
• Rouge/rouge2: 0.0
• Rouge/rougel: 0.0
• Rouge/rougelsum: 0.0
• Bertscore/bertscore-precision: 0.0
• Bertscore/bertscore-recall: 0.0
• Bertscore/bertscore-f1: 0.0
• Meteor: 0.0
• Gen Len: 0.0

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: 0.001
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 4
• 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
1.0524	1.0	217	1.9798	0.4548	0.2064	0.3831	0.3848	0.8919	0.8942	0.8929	0.4177	41.2636
1.4167	2.0	434	1.9903	0.4327	0.1886	0.3657	0.3672	0.8947	0.8843	0.8893	0.3692	33.8545
1.3767	3.0	651	2.0041	0.4527	0.2062	0.3851	0.3862	0.8945	0.8927	0.8934	0.4073	38.0727
1.0766	4.0	868	2.0268	0.4611	0.2088	0.3905	0.3915	0.8985	0.8923	0.8952	0.4052	36.4182
1.0071	5.0	1085	2.3584	0.4183	0.1611	0.3443	0.3455	0.8901	0.8858	0.8877	0.3591	36.9909
1.1108	6.0	1302	2.5739	0.4169	0.1599	0.3463	0.3463	0.8895	0.8835	0.8861	0.3516	35.2636
1.2043	7.0	1519	2.7884	0.3995	0.1628	0.3339	0.3347	0.8717	0.8827	0.876	0.341	38.7364
1.24	8.0	1736	2.7884	0.3995	0.1628	0.3339	0.3347	0.8717	0.8827	0.876	0.341	38.7364
1.2303	9.0	1953	2.7884	0.3995	0.1628	0.3339	0.3347	0.8717	0.8827	0.876	0.341	38.7364
1.218	10.0	2170	2.7884	0.3995	0.1628	0.3339	0.3347	0.8717	0.8827	0.876	0.341	38.7364
1.2135	11.0	2387	2.7884	0.3995	0.1628	0.3339	0.3347	0.8717	0.8827	0.876	0.341	38.7364
1.2276	12.0	2604	2.7884	0.3995	0.1628	0.3339	0.3347	0.8717	0.8827	0.876	0.341	38.7364
1.2104	13.0	2821	2.7884	0.3995	0.1628	0.3339	0.3347	0.8717	0.8827	0.876	0.341	38.7364
1.2409	14.0	3038	2.7884	0.3995	0.1628	0.3339	0.3347	0.8717	0.8827	0.876	0.341	38.7364
4.1714	15.0	3255	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	16.0	3472	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	17.0	3689	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	18.0	3906	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	19.0	4123	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.0	20.0	4340	nan	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

Framework versions

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