flan-t5-rouge-durga-3

This model is a fine-tuned version of google/flan-t5-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0024
• Rouge1: 0.5907
• Rouge2: 0.5623
• Rougel: 0.5912
• Rougelsum: 0.5922

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: 0.0003
• 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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
2.4258	1.0	50	2.0073	0.2733	0.1044	0.2514	0.2517
2.2156	2.0	100	1.5972	0.2600	0.0904	0.2466	0.2473
1.9538	3.0	150	1.2838	0.2999	0.1401	0.2867	0.2877
1.2774	4.0	200	1.0377	0.3370	0.1809	0.3253	0.3273
1.2395	5.0	250	0.8060	0.3688	0.2162	0.3561	0.3589
1.6069	6.0	300	0.6646	0.3898	0.2558	0.3752	0.3774
1.2469	7.0	350	0.5189	0.4014	0.2612	0.3875	0.3896
0.664	8.0	400	0.4060	0.4506	0.3294	0.4380	0.4402
1.8862	9.0	450	0.3131	0.4657	0.3734	0.4600	0.4623
0.3403	10.0	500	0.2534	0.4662	0.3662	0.4602	0.4622
0.5261	11.0	550	0.1996	0.4984	0.4243	0.4951	0.4967
1.0378	12.0	600	0.1467	0.5047	0.4431	0.5035	0.5055
0.218	13.0	650	0.1176	0.4912	0.4286	0.4883	0.4924
0.5121	14.0	700	0.0843	0.5418	0.4951	0.5416	0.5427
0.4996	15.0	750	0.0679	0.5645	0.5231	0.5612	0.5645
0.2511	16.0	800	0.0521	0.5426	0.4922	0.5399	0.5434
0.3803	17.0	850	0.0395	0.5607	0.5232	0.5604	0.5616
0.0706	18.0	900	0.0261	0.5816	0.5460	0.5824	0.5838
0.1628	19.0	950	0.0193	0.5833	0.5541	0.5843	0.5848
0.1777	20.0	1000	0.0141	0.5833	0.5524	0.5831	0.5853
0.1254	21.0	1050	0.0122	0.5849	0.5554	0.5842	0.5866
0.2481	22.0	1100	0.0109	0.5916	0.5616	0.5915	0.5932
0.0604	23.0	1150	0.0066	0.5912	0.5623	0.5909	0.5926
0.1083	24.0	1200	0.0048	0.5902	0.5611	0.5904	0.5916
0.2921	25.0	1250	0.0051	0.5890	0.5602	0.5891	0.5904
0.1846	26.0	1300	0.0037	0.5902	0.5618	0.5907	0.5920
0.1952	27.0	1350	0.0029	0.5907	0.5623	0.5912	0.5922
0.0419	28.0	1400	0.0026	0.5897	0.5611	0.5898	0.5914
0.1353	29.0	1450	0.0025	0.5906	0.5618	0.5902	0.5916
0.0072	30.0	1500	0.0024	0.5907	0.5623	0.5912	0.5922

Framework versions

• Transformers 4.45.2
• Pytorch 2.5.0+cu121
• Datasets 3.0.2
• Tokenizers 0.20.1