t5-abs-2209-2245-lr-0.001-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: 2.6565
• Rouge/rouge1: 0.3034
• Rouge/rouge2: 0.116
• Rouge/rougel: 0.2608
• Rouge/rougelsum: 0.2609
• Bertscore/bertscore-precision: 0.789
• Bertscore/bertscore-recall: 0.8697
• Bertscore/bertscore-f1: 0.8251
• Meteor: 0.2559
• Gen Len: 60.4545

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: 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
1.5977	0.9885	43	1.8385	0.4519	0.2025	0.3799	0.3812	0.8943	0.8922	0.8931	0.4053	37.9273
1.1494	2.0	87	1.7744	0.4543	0.2046	0.3787	0.3803	0.8964	0.8918	0.8939	0.3972	36.2
1.7877	2.9885	130	2.8310	0.3344	0.1165	0.2735	0.2749	0.8579	0.8794	0.8676	0.3013	46.5273
2.83	4.0	174	2.7267	0.3339	0.1241	0.2813	0.2823	0.8636	0.8795	0.8709	0.3166	47.9182
2.6683	4.9885	217	2.6435	0.3296	0.1231	0.2791	0.2795	0.8478	0.8781	0.8615	0.3045	50.5455
2.5709	6.0	261	2.6193	0.3149	0.1164	0.2677	0.2679	0.828	0.8742	0.8488	0.2887	53.7818
2.6182	6.9885	304	2.6497	0.3028	0.1144	0.259	0.2598	0.7963	0.8706	0.8297	0.2619	59.4727
2.5553	8.0	348	2.6565	0.2994	0.1138	0.2581	0.2585	0.7895	0.8685	0.8248	0.2509	59.8909
2.6243	8.9885	391	2.6565	0.3001	0.1148	0.2576	0.2581	0.7911	0.8691	0.826	0.2547	60.1182
2.6506	10.0	435	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545
2.6527	10.9885	478	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545
2.6511	12.0	522	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545
2.612	12.9885	565	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545
2.6429	14.0	609	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545
2.6948	14.9885	652	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545
2.5908	16.0	696	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545
2.5995	16.9885	739	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545
2.5835	18.0	783	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545
2.6417	18.9885	826	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545
2.5769	19.7701	860	2.6565	0.3034	0.116	0.2608	0.2609	0.789	0.8697	0.8251	0.2559	60.4545

Framework versions

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