mt5-rouge-durga-q1-clean

This model is a fine-tuned version of google/mt5-base on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.7819
• Rouge1: 0.3074
• Rouge2: 0.0953
• Rougel: 0.3026
• Rougelsum: 0.3008

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: 20
• eval_batch_size: 20
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
15.8442	1.0	3	11.1246	0.0148	0.0015	0.0152	0.0151
13.0661	2.0	6	9.3553	0.0226	0.0052	0.0219	0.0217
11.7048	3.0	9	8.0317	0.0198	0.0029	0.0177	0.0190
8.87	4.0	12	7.1382	0.0461	0.0105	0.0423	0.0406
11.0893	5.0	15	6.7905	0.0611	0.0106	0.0512	0.0503
9.8787	6.0	18	6.5255	0.0900	0.0224	0.0800	0.0782
9.8189	7.0	21	6.7007	0.0944	0.0231	0.0876	0.0861
8.2022	8.0	24	6.2109	0.0953	0.0227	0.0899	0.0910
8.5899	9.0	27	5.9520	0.0965	0.0171	0.0897	0.0914
7.5305	10.0	30	5.5748	0.0855	0.0157	0.0841	0.0821
7.0381	11.0	33	5.2219	0.0622	0.0095	0.0592	0.0585
6.675	12.0	36	4.8006	0.0529	0.0048	0.0499	0.0489
7.4134	13.0	39	4.3795	0.0693	0.0079	0.0635	0.0610
5.8722	14.0	42	3.9322	0.1060	0.0128	0.1003	0.1009
4.5875	15.0	45	3.5017	0.1012	0.0069	0.0968	0.0968
5.3675	16.0	48	3.1927	0.0944	0.0020	0.0915	0.0913
4.2999	17.0	51	2.8956	0.0890	0.0091	0.0831	0.0849
4.3349	18.0	54	2.7138	0.1164	0.0074	0.1114	0.1128
3.9688	19.0	57	2.5350	0.1122	0.0	0.1122	0.1121
4.2931	20.0	60	2.4138	0.1122	0.0	0.1122	0.1121
3.8427	21.0	63	2.3127	0.1122	0.0	0.1122	0.1121
3.2991	22.0	66	2.2054	0.1122	0.0	0.1122	0.1121
3.1351	23.0	69	2.1069	0.1122	0.0	0.1122	0.1121
3.023	24.0	72	2.0208	0.1142	0.0	0.1140	0.1139
3.4366	25.0	75	1.9500	0.1793	0.0352	0.1713	0.1711
2.7941	26.0	78	1.9068	0.3104	0.0909	0.3016	0.3005
2.9454	27.0	81	1.8419	0.3086	0.0940	0.3009	0.2984
2.6117	28.0	84	1.8775	0.3135	0.0955	0.3086	0.3067
2.6785	29.0	87	1.7772	0.3020	0.0946	0.2987	0.2968
2.7523	30.0	90	1.7819	0.3074	0.0953	0.3026	0.3008

Framework versions

• Transformers 4.46.1
• Pytorch 2.5.0+cu121
• Datasets 3.0.2
• Tokenizers 0.20.1