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	300	0.2824	0.0033	0.3970	0.3437	0.2930	0.2280	59.9496	0.9410	0.9115	0.8792	0.8422	7.2709
0.4472	2.0	600	0.2520	0.0033	0.6272	0.5542	0.4799	0.3809	36.6919	0.9524	0.9274	0.8990	0.8663	6.1255
0.4472	3.0	900	0.2422	0.0033	0.7249	0.6515	0.5749	0.4743	31.9060	0.9464	0.9220	0.8935	0.8595	6.6484
0.0958	4.0	1200	0.2221	0.0033	0.8251	0.7588	0.6904	0.5818	24.7691	0.9631	0.9420	0.9166	0.8865	4.9552
0.0543	5.0	1500	0.2228	0.0033	0.7720	0.7056	0.6371	0.5350	27.7918	0.9561	0.9341	0.9088	0.8782	5.9761
0.0543	6.0	1800	0.2289	0.0033	0.8046	0.7289	0.6549	0.5539	26.3644	0.9444	0.9218	0.8964	0.8665	7.6195
0.0339	7.0	2100	0.2397	0.0033	0.7718	0.6886	0.6044	0.4846	32.4097	0.9181	0.8922	0.8640	0.8301	12.3506
0.0339	8.0	2400	0.2501	0.0033	0.7656	0.6927	0.6164	0.5111	30.7305	0.9371	0.9147	0.8900	0.8595	8.5408
0.0279	9.0	2700	0.2256	0.0033	0.8464	0.7802	0.7114	0.6030	23.0898	0.9340	0.9123	0.8887	0.8589	8.4910
0.0197	10.0	3000	0.2364	0.0033	0.8317	0.7717	0.7034	0.5941	24.0134	0.8519	0.8202	0.7860	0.7427	19.8456
0.0197	11.0	3300	0.2382	0.0033	0.8315	0.7693	0.7025	0.5981	23.0059	0.9456	0.9243	0.9004	0.8705	7.3705
0.0181	12.0	3600	0.2367	0.0033	0.8180	0.7518	0.6761	0.5627	27.1201	0.9318	0.8973	0.8611	0.8211	8.9890
0.0181	13.0	3900	0.2518	0.0033	0.7694	0.7032	0.6303	0.5205	28.9673	0.9285	0.8971	0.8646	0.8290	9.5369
0.0142	14.0	4200	0.2287	0.0033	0.8210	0.7598	0.6943	0.5892	24.5172	0.9420	0.9153	0.8880	0.8565	7.9681
0.0126	15.0	4500	0.2474	0.0033	0.8212	0.7575	0.6856	0.5748	25.1050	0.9259	0.8985	0.8707	0.8386	9.8606
0.0126	16.0	4800	0.2625	0.0033	0.7986	0.7340	0.6661	0.5594	26.1125	0.9557	0.9332	0.9067	0.8762	6.0010
0.0086	17.0	5100	0.2426	0.0033	0.8044	0.7325	0.6572	0.5571	26.0285	0.9539	0.9296	0.9023	0.8703	6.0010
0.0086	18.0	5400	0.2505	0.0033	0.7994	0.7270	0.6517	0.5444	27.7078	0.9459	0.9204	0.8927	0.8604	7.6693
0.0084	19.0	5700	0.2514	0.0033	0.8252	0.7541	0.6807	0.5752	25.9446	0.9150	0.8761	0.8384	0.7990	11.4791
0.007	20.0	6000	0.2542	0.0033	0.7730	0.7018	0.6303	0.5289	30.3946	0.9068	0.8760	0.8430	0.8054	12.4253
0.007	21.0	6300	0.2555	0.0033	0.7984	0.7275	0.6544	0.5479	28.5474	0.9444	0.9152	0.8869	0.8541	7.8934
0.0054	22.0	6600	0.2483	0.0033	0.7941	0.7271	0.6557	0.5480	27.6238	0.9478	0.9223	0.8944	0.8618	7.2460
0.0054	23.0	6900	0.2480	0.0033	0.8055	0.7460	0.6826	0.5782	25.1889	0.9364	0.9063	0.8754	0.8396	9.0139
0.0056	24.0	7200	0.2541	0.0033	0.8139	0.7519	0.6847	0.5792	24.7691	0.9461	0.9212	0.8947	0.8628	7.2211
0.0037	25.0	7500	0.2527	0.0033	0.8172	0.7523	0.6841	0.5763	26.7842	0.9294	0.9079	0.8829	0.8519	9.3376
0.0037	26.0	7800	0.2594	0.0033	0.8075	0.7417	0.6699	0.5596	26.8682	0.9366	0.9163	0.8920	0.8619	8.4163
0.0037	27.0	8100	0.2485	0.0033	0.8050	0.7420	0.6714	0.5611	25.7767	0.9345	0.9071	0.8783	0.8439	8.9392
0.0037	28.0	8400	0.2485	0.0033	0.8028	0.7389	0.6691	0.5607	26.5323	0.9385	0.9125	0.8847	0.8510	8.5408
0.0032	29.0	8700	0.2499	0.0033	0.8034	0.7409	0.6721	0.5655	26.0285	0.9421	0.9171	0.8901	0.8570	8.0428
0.0027	30.0	9000	0.2506	0.0033	0.8018	0.7398	0.6719	0.5648	26.1965	0.9404	0.9147	0.8877	0.8547	8.2918

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

vania2911/exp1_10partition_modelo12000

exp1_10partition_modelo12000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
exp1_10partition_modelo12000