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	225	0.3088	0.0038	0.2992	0.2390	0.1792	0.1099	88.3090	0.9517	0.9241	0.8923	0.8589	5.9826
No log	2.0	450	0.2691	0.0038	0.8584	0.7799	0.6792	0.4958	20.7724	0.9640	0.9423	0.9152	0.8855	4.6048
0.4907	3.0	675	0.2479	0.0038	0.8932	0.8218	0.7138	0.5437	17.9541	0.9705	0.9507	0.9270	0.9004	3.5533
0.4907	4.0	900	0.2521	0.0038	0.8189	0.7413	0.6413	0.4898	19.8330	0.9676	0.9460	0.9206	0.8919	4.0972
0.0885	5.0	1125	0.2573	0.0038	0.8680	0.8041	0.7113	0.5416	19.2067	0.9694	0.9507	0.9264	0.8986	3.6621
0.0885	6.0	1350	0.2423	0.0038	0.8640	0.7893	0.6870	0.5151	21.7119	0.9737	0.9557	0.9321	0.9050	3.1182
0.047	7.0	1575	0.2308	0.0038	0.8951	0.8277	0.7299	0.5528	19.9374	0.9768	0.9603	0.9389	0.9142	2.9369
0.047	8.0	1800	0.2880	0.0038	0.8596	0.7856	0.6788	0.4906	18.9979	0.9724	0.9534	0.9289	0.9022	3.3358
0.0296	9.0	2025	0.2530	0.0038	0.8804	0.8172	0.7277	0.5684	21.1900	0.9743	0.9561	0.9328	0.9067	3.2270
0.0296	10.0	2250	0.2573	0.0038	0.8697	0.7896	0.6886	0.5156	23.5908	0.9756	0.9579	0.9351	0.9096	2.9732
0.0296	11.0	2475	0.2681	0.0038	0.8682	0.7926	0.6893	0.5100	24.4259	0.9640	0.9453	0.9208	0.8930	4.3147
0.0208	12.0	2700	0.2789	0.0038	0.8891	0.8304	0.7371	0.5664	18.6848	0.9756	0.9587	0.9356	0.9096	2.9369
0.0208	13.0	2925	0.2463	0.0038	0.8941	0.8362	0.7425	0.5816	17.9541	0.9771	0.9590	0.9350	0.9081	2.8281
0.0153	14.0	3150	0.2701	0.0038	0.8518	0.7822	0.6871	0.5221	24.4259	0.9734	0.9559	0.9327	0.9067	3.1545
0.0153	15.0	3375	0.2805	0.0038	0.8769	0.8143	0.7193	0.5422	19.2067	0.9768	0.9597	0.9368	0.9113	2.8281
0.0128	16.0	3600	0.2906	0.0038	0.8824	0.8105	0.7106	0.5389	21.9207	0.9768	0.9595	0.9366	0.9116	2.9007
0.0128	17.0	3825	0.2870	0.0038	0.8693	0.7938	0.6917	0.5221	22.4426	0.9769	0.9603	0.9385	0.9140	2.7556
0.0101	18.0	4050	0.2758	0.0038	0.8821	0.8130	0.7135	0.5438	20.5637	0.9749	0.9563	0.9330	0.9069	3.1545
0.0101	19.0	4275	0.2702	0.0038	0.8837	0.8193	0.7213	0.5464	19.6242	0.9750	0.9583	0.9357	0.9094	2.9369
0.0074	20.0	4500	0.2731	0.0038	0.8653	0.7935	0.6921	0.5283	22.7557	0.9747	0.9577	0.9352	0.9094	3.0457
0.0074	21.0	4725	0.2785	0.0038	0.8894	0.8259	0.7280	0.5578	18.9979	0.9765	0.9601	0.9376	0.9127	2.8281
0.0074	22.0	4950	0.2906	0.0038	0.8912	0.8304	0.7331	0.5552	18.8935	0.9747	0.9583	0.9364	0.9115	2.9732
0.006	23.0	5175	0.2898	0.0038	0.8825	0.8233	0.7253	0.5507	18.3716	0.9759	0.9597	0.9378	0.9126	2.8644
0.006	24.0	5400	0.2851	0.0038	0.8795	0.8226	0.7266	0.5447	18.5804	0.9766	0.9610	0.9395	0.9146	2.7919
0.0054	25.0	5625	0.2709	0.0038	0.8864	0.8186	0.7177	0.5434	19.3111	0.9769	0.9615	0.9403	0.9159	2.7919
0.0054	26.0	5850	0.2767	0.0038	0.8884	0.8215	0.7243	0.5477	19.5198	0.9772	0.9616	0.9405	0.9161	2.7556
0.0044	27.0	6075	0.2802	0.0038	0.8760	0.8060	0.7042	0.5237	20.0418	0.9769	0.9616	0.9402	0.9153	2.6831
0.0044	28.0	6300	0.2765	0.0038	0.8789	0.8090	0.7084	0.5317	20.2505	0.9775	0.9622	0.9411	0.9168	2.6831
0.0036	29.0	6525	0.2785	0.0038	0.8808	0.8111	0.7094	0.5294	20.1461	0.9775	0.9622	0.9411	0.9166	2.6831
0.0036	30.0	6750	0.2801	0.0038	0.8789	0.8068	0.7054	0.5267	20.5637	0.9784	0.9635	0.9427	0.9188	2.5743

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

vania2911/exp2_10partition_modelo9000

exp2_10partition_modelo9000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
exp2_10partition_modelo9000