learning_rate: 0.0001
train_batch_size: 32
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Bleu 1 Msl	Bleu 2 Msl	Bleu 3 Msl	Bleu 4 Msl	Ter Msl	Bleu 1 Asl	Bleu 2 Asl	Bleu 3 Asl	Bleu 4 Asl	Ter Asl
No log	1.0	150	1.4201	0.0035	0.5855	0.4688	0.3803	0.2617	49.7061	0.8418	0.7628	0.6958	0.6270	19.7203
No log	2.0	300	1.0165	0.0035	0.7396	0.6342	0.5464	0.4526	37.6994	0.8953	0.8417	0.7961	0.7461	13.1469
No log	3.0	450	0.8755	0.0035	0.7379	0.6574	0.5803	0.4725	35.7683	0.9177	0.8716	0.8293	0.7826	10.7692
1.6824	4.0	600	0.8238	0.0035	0.7738	0.6926	0.6179	0.5189	30.9824	0.9363	0.9006	0.8660	0.8267	8.2517
1.6824	5.0	750	0.8042	0.0035	0.7149	0.6312	0.5438	0.4332	35.1805	0.9374	0.9093	0.8824	0.8507	7.9720
1.6824	6.0	900	0.8480	0.0035	0.7469	0.6742	0.6091	0.5018	30.3107	0.9375	0.9025	0.8698	0.8320	7.8322
0.2441	7.0	1050	0.8531	0.0035	0.6312	0.5500	0.4770	0.3797	41.4777	0.9406	0.9063	0.8734	0.8372	7.6923
0.2441	8.0	1200	0.8495	0.0035	0.7302	0.6401	0.5653	0.4614	36.9437	0.9477	0.9165	0.8873	0.8551	6.9231
0.2441	9.0	1350	0.8623	0.0035	0.6935	0.6149	0.5451	0.4552	39.4626	0.9402	0.9086	0.8778	0.8415	7.6224
0.1022	10.0	1500	0.8533	0.0035	0.6700	0.6052	0.5381	0.4471	37.9513	0.9453	0.9139	0.8848	0.8516	6.9231
0.1022	11.0	1650	0.9126	0.0035	0.6731	0.5920	0.5202	0.4231	38.9589	0.9525	0.9238	0.8951	0.8631	6.0839
0.1022	12.0	1800	0.9068	0.0035	0.7141	0.6329	0.5539	0.4452	37.1956	0.9473	0.9165	0.8869	0.8533	6.7832
0.1022	13.0	1950	0.9214	0.0035	0.6425	0.5591	0.4849	0.3882	43.6608	0.9471	0.9164	0.8881	0.8574	6.9930
0.0478	14.0	2100	0.9292	0.0035	0.6367	0.5527	0.4761	0.3818	42.1495	0.9409	0.9071	0.8741	0.8374	7.9021
0.0478	15.0	2250	0.9934	0.0035	0.6377	0.5528	0.4728	0.3743	42.6532	0.9438	0.9115	0.8803	0.8446	7.1329
0.0478	16.0	2400	0.9738	0.0035	0.6457	0.5657	0.4902	0.3998	42.5693	0.9443	0.9124	0.8817	0.8465	7.3427
0.0274	17.0	2550	0.8840	0.0035	0.6418	0.5598	0.4831	0.3861	41.4777	0.9474	0.9185	0.8896	0.8553	6.8531
0.0274	18.0	2700	0.9120	0.0035	0.6428	0.5599	0.4832	0.3895	43.2410	0.9442	0.9110	0.8812	0.8489	7.3427
0.0274	19.0	2850	0.9104	0.0035	0.6790	0.6017	0.5266	0.4279	40.4702	0.9480	0.9174	0.8880	0.8555	6.7133
0.017	20.0	3000	0.9158	0.0035	0.6488	0.5616	0.4771	0.3772	40.8900	0.9425	0.9084	0.8769	0.8420	7.6224
0.017	21.0	3150	0.9166	0.0035	0.6541	0.5670	0.4891	0.3918	40.2183	0.9431	0.9103	0.8804	0.8472	7.4825
0.017	22.0	3300	0.8951	0.0035	0.6413	0.5573	0.4797	0.3779	41.3938	0.9427	0.9095	0.8785	0.8441	7.0629
0.017	23.0	3450	0.9018	0.0035	0.6557	0.5731	0.4941	0.3926	41.1419	0.9444	0.9122	0.8818	0.8481	7.1329
0.012	24.0	3600	0.8967	0.0035	0.6830	0.6007	0.5199	0.4124	37.9513	0.9479	0.9183	0.8900	0.8582	6.6434
0.012	25.0	3750	0.9093	0.0035	0.6570	0.5781	0.5005	0.3941	39.5466	0.9504	0.9225	0.8954	0.8646	6.3636
0.012	26.0	3900	0.8968	0.0035	0.6863	0.6044	0.5242	0.4228	38.3711	0.9474	0.9174	0.8889	0.8570	6.7133
0.0089	27.0	4050	0.8918	0.0035	0.6705	0.5902	0.5122	0.4065	39.7145	0.9498	0.9196	0.8913	0.8597	6.4336
0.0089	28.0	4200	0.8972	0.0035	0.6690	0.5861	0.5066	0.4014	39.7145	0.9480	0.9176	0.8887	0.8564	6.7133
0.0089	29.0	4350	0.9027	0.0035	0.6605	0.5778	0.4965	0.3926	40.8060	0.9474	0.9170	0.8876	0.8550	6.7133
0.0075	30.0	4500	0.8992	0.0035	0.6606	0.5775	0.4969	0.3932	40.3862	0.9480	0.9180	0.8889	0.8566	6.6434

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

vania2911/exp1_10partition_modelo_msl6000

exp1_10partition_modelo_msl6000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
exp1_10partition_modelo_msl6000