mt5-rouge-durga-2

This model is a fine-tuned version of google/mt5-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0126
• Rouge1: 0.6270
• Rouge2: 0.6003
• Rougel: 0.6244
• Rougelsum: 0.6247

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
4.989	1.0	85	2.8197	0.2164	0.0941	0.1882	0.1883
3.116	2.0	170	2.0798	0.3122	0.1588	0.2604	0.2604
2.8357	3.0	255	1.5681	0.3446	0.1935	0.2953	0.2955
1.7776	4.0	340	1.1806	0.3324	0.1952	0.2895	0.2904
1.1881	5.0	425	0.9407	0.3533	0.2228	0.3088	0.3091
1.8511	6.0	510	0.6826	0.3971	0.2700	0.3644	0.3636
1.7178	7.0	595	0.5128	0.4194	0.3120	0.3894	0.3891
1.2772	8.0	680	0.3878	0.4590	0.3619	0.4311	0.4302
1.3577	9.0	765	0.2709	0.4729	0.3881	0.4499	0.4497
0.8291	10.0	850	0.2005	0.5006	0.4276	0.4748	0.4747
0.6825	11.0	935	0.1616	0.5411	0.4732	0.5215	0.5224
0.5006	12.0	1020	0.1182	0.5348	0.4782	0.5200	0.5196
0.5193	13.0	1105	0.1027	0.5446	0.4910	0.5269	0.5286
0.3933	14.0	1190	0.0881	0.5685	0.5200	0.5535	0.5548
0.1584	15.0	1275	0.0708	0.5719	0.5327	0.5629	0.5645
0.3657	16.0	1360	0.0646	0.5763	0.5315	0.5648	0.5659
0.2731	17.0	1445	0.0525	0.5908	0.5500	0.5844	0.5844
0.3466	18.0	1530	0.0511	0.5971	0.5596	0.5873	0.5886
0.1892	19.0	1615	0.0384	0.6044	0.5675	0.5991	0.5995
0.1684	20.0	1700	0.0328	0.6066	0.5744	0.6046	0.6050
0.0691	21.0	1785	0.0295	0.6057	0.5726	0.6020	0.6027
0.0326	22.0	1870	0.0243	0.6167	0.5872	0.6138	0.6146
0.1872	23.0	1955	0.0195	0.6188	0.5899	0.6149	0.6160
0.1372	24.0	2040	0.0183	0.6253	0.5961	0.6227	0.6233
0.0621	25.0	2125	0.0166	0.6239	0.5957	0.6211	0.6225
0.2539	26.0	2210	0.0161	0.6217	0.5926	0.6191	0.6200
0.2532	27.0	2295	0.0166	0.6195	0.5910	0.6166	0.6173
0.1158	28.0	2380	0.0145	0.6223	0.5943	0.6196	0.6202
0.3496	29.0	2465	0.0132	0.6241	0.5957	0.6212	0.6217
0.059	30.0	2550	0.0126	0.6270	0.6003	0.6244	0.6247

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Datasets 3.0.0
• Tokenizers 0.19.1