mt5-simplification-spanish-clara-med

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

• Loss: 1.9610
• Rouge1: 33.7922
• Rouge2: 19.5758
• Rougel: 31.3737
• Rougelsum: 31.3428

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: 5.6e-05
• train_batch_size: 16
• eval_batch_size: 16
• 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
No log	1.0	190	2.6876	32.236	18.2352	29.7852	29.7539
No log	2.0	380	2.4617	32.8521	18.9712	30.4958	30.4635
3.3018	3.0	570	2.3487	33.2554	19.3441	30.9036	30.8525
3.3018	4.0	760	2.2711	33.0105	19.01	30.6851	30.5767
2.7431	5.0	950	2.2254	33.1301	18.9618	30.6744	30.6284
2.7431	6.0	1140	2.1847	33.3701	19.1884	30.9138	30.8611
2.7431	7.0	1330	2.1443	33.3158	19.101	30.8317	30.7747
2.5154	8.0	1520	2.1072	33.1638	19.0139	30.7295	30.7162
2.5154	9.0	1710	2.0989	33.4925	19.2107	31.0253	30.9908
2.3763	10.0	1900	2.0709	33.3007	18.9519	30.847	30.8018
2.3763	11.0	2090	2.0631	33.4689	19.1995	31.0712	31.0327
2.3763	12.0	2280	2.0418	33.2536	19.027	30.898	30.8695
2.2811	13.0	2470	2.0345	33.5097	19.2219	31.1057	31.0683
2.2811	14.0	2660	2.0185	33.3544	19.1241	30.913	30.8873
2.2173	15.0	2850	2.0138	33.3856	19.2065	31.0173	30.9447
2.2173	16.0	3040	2.0019	33.4035	19.1803	31.0154	30.981
2.2173	17.0	3230	1.9977	33.4059	19.3078	31.1196	31.0692
2.1612	18.0	3420	1.9883	33.5097	19.3637	31.0966	31.0554
2.1612	19.0	3610	1.9828	33.4965	19.2754	31.1267	31.1021
2.1115	20.0	3800	1.9834	33.7514	19.5325	31.2833	31.2418
2.1115	21.0	3990	1.9754	33.6193	19.429	31.2721	31.2267
2.1115	22.0	4180	1.9716	33.5212	19.3637	31.1326	31.1162
2.0824	23.0	4370	1.9667	33.5156	19.3223	31.1023	31.0709
2.0824	24.0	4560	1.9735	33.6089	19.3842	31.1539	31.1419
2.0657	25.0	4750	1.9674	33.6317	19.4044	31.2361	31.2222
2.0657	26.0	4940	1.9617	33.745	19.5099	31.3061	31.2643
2.0657	27.0	5130	1.9613	33.7798	19.5496	31.3761	31.3356
2.0511	28.0	5320	1.9635	33.8568	19.594	31.4454	31.4141
2.0511	29.0	5510	1.9609	33.805	19.5962	31.393	31.3493
2.0377	30.0	5700	1.9610	33.7922	19.5758	31.3737	31.3428

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0
• Datasets 2.8.0
• Tokenizers 0.12.1