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	1.0887	0.0034	0.4826	0.2722	0.1227	0.0381	77.7403	0.9557	0.9266	0.8963	0.8624	5.6523
No log	2.0	450	0.9520	0.0034	0.2741	0.1653	0.0996	0.0588	100.3373	0.9598	0.9337	0.9067	0.8761	5.0480
0.4636	3.0	675	0.8482	0.0034	0.0798	0.0517	0.0356	0.0223	319.4772	0.9711	0.9506	0.9278	0.9006	3.4838
0.4636	4.0	900	0.8447	0.0034	0.5112	0.3597	0.2465	0.1454	70.4047	0.9718	0.9515	0.9284	0.9011	3.4838
0.0795	5.0	1125	0.9296	0.0034	0.6046	0.4295	0.3023	0.1991	61.9730	0.9746	0.9569	0.9361	0.9110	3.1283
0.0795	6.0	1350	0.9205	0.0034	0.2045	0.1377	0.0924	0.0525	129.8482	0.9730	0.9533	0.9299	0.9022	3.4127
0.0444	7.0	1575	0.8835	0.0034	0.6634	0.4998	0.3640	0.2243	46.7960	0.9690	0.9487	0.9260	0.8997	3.6260
0.0444	8.0	1800	0.9059	0.0034	0.3985	0.2859	0.2046	0.1390	82.6307	0.9688	0.9503	0.9291	0.9036	3.8038
0.0295	9.0	2025	0.9123	0.0034	0.6423	0.4784	0.3442	0.2388	56.0708	0.9721	0.9523	0.9295	0.9023	3.3772
0.0295	10.0	2250	0.9518	0.0034	0.6213	0.4665	0.3392	0.2180	59.7808	0.9734	0.9549	0.9333	0.9069	3.1283
0.0295	11.0	2475	0.8786	0.0034	0.6458	0.4985	0.3877	0.2960	51.1804	0.9761	0.9584	0.9374	0.9122	2.8439
0.0191	12.0	2700	0.9260	0.0034	0.6155	0.4650	0.3479	0.2512	49.1568	0.9758	0.9586	0.9378	0.9128	2.9150
0.0191	13.0	2925	0.9082	0.0034	0.6272	0.4727	0.3485	0.2391	53.1197	0.9762	0.9588	0.9375	0.9123	2.8439
0.0156	14.0	3150	0.9289	0.0034	0.6416	0.4961	0.3760	0.2734	49.5784	0.9739	0.9550	0.9334	0.9076	3.1994
0.0156	15.0	3375	0.8962	0.0034	0.6708	0.5202	0.4034	0.3018	52.0236	0.9740	0.9548	0.9322	0.9052	3.1283
0.0135	16.0	3600	0.9321	0.0034	0.6439	0.5017	0.3966	0.3077	47.3862	0.9740	0.9548	0.9324	0.9059	3.0217
0.0135	17.0	3825	0.9052	0.0034	0.6490	0.4953	0.3741	0.2675	54.5531	0.9733	0.9545	0.9327	0.9068	3.2350
0.0104	18.0	4050	0.8770	0.0034	0.6561	0.5057	0.3883	0.2808	51.2648	0.9734	0.9545	0.9333	0.9079	3.1283
0.0104	19.0	4275	0.9140	0.0034	0.6677	0.5107	0.3817	0.2643	50.0	0.9706	0.9515	0.9295	0.9027	3.4483
0.0078	20.0	4500	0.9122	0.0034	0.6495	0.4980	0.3803	0.2745	47.4705	0.9755	0.9577	0.9373	0.9129	2.8795
0.0078	21.0	4725	0.8904	0.0034	0.6584	0.5046	0.3862	0.2857	50.3373	0.9768	0.9603	0.9413	0.9185	2.7728
0.0078	22.0	4950	0.8965	0.0034	0.6587	0.5085	0.3895	0.2876	50.7589	0.9771	0.9602	0.9404	0.9169	2.7728
0.0064	23.0	5175	0.9242	0.0034	0.6522	0.4916	0.3607	0.2477	53.1197	0.9771	0.9598	0.9395	0.9150	2.7728
0.0064	24.0	5400	0.8987	0.0034	0.6615	0.5090	0.3902	0.2835	48.5666	0.9765	0.9598	0.9397	0.9154	2.7728
0.0046	25.0	5625	0.9078	0.0034	0.6654	0.5086	0.3844	0.2725	48.6509	0.9768	0.9603	0.9407	0.9169	2.7373
0.0046	26.0	5850	0.8952	0.0034	0.6747	0.5153	0.3926	0.2776	53.4570	0.9762	0.9594	0.9394	0.9152	2.8084
0.0044	27.0	6075	0.8998	0.0034	0.6814	0.5294	0.4121	0.3009	49.4941	0.9765	0.9595	0.9390	0.9142	2.7728
0.0044	28.0	6300	0.9095	0.0034	0.6751	0.5218	0.4000	0.2880	51.0118	0.9768	0.9599	0.9395	0.9149	2.7728
0.0039	29.0	6525	0.9113	0.0034	0.6728	0.5201	0.4009	0.2895	50.2530	0.9768	0.9599	0.9399	0.9158	2.7728
0.0039	30.0	6750	0.9123	0.0034	0.6714	0.5183	0.3989	0.2878	50.9275	0.9768	0.9599	0.9397	0.9154	2.7728

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

vania2911/exp3_10partition_modelo9000

exp3_10partition_modelo9000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
exp3_10partition_modelo9000