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	Ter Msl	Bleu 1 Asl	Bleu 2 Asl	Bleu 3 Asl	Bleu 4 Asl	Ter Asl
No log	1.0	75	0.2051	0.0031	100	0.9471	0.9166	0.8870	0.8548	6.7832
No log	2.0	150	0.1380	0.0031	100	0.9564	0.9316	0.9065	0.8757	5.1748
No log	3.0	225	0.1438	0.0031	100	0.9603	0.9361	0.9100	0.8804	5.1748
No log	4.0	300	0.1273	0.0031	100	0.9671	0.9470	0.9254	0.8985	3.9860
No log	5.0	375	0.1163	0.0031	100	0.9730	0.9557	0.9358	0.9114	3.4266
No log	6.0	450	0.1357	0.0031	100	0.9681	0.9474	0.9252	0.8985	3.9860
0.2716	7.0	525	0.1560	0.0031	100	0.9700	0.9521	0.9321	0.9069	3.6364
0.2716	8.0	600	0.1263	0.0031	100	0.9748	0.9585	0.9403	0.9171	2.9371
0.2716	9.0	675	0.1407	0.0031	100	0.9802	0.9663	0.9503	0.9300	2.3776
0.2716	10.0	750	0.1396	0.0031	100	0.9778	0.9633	0.9449	0.9217	2.7273
0.2716	11.0	825	0.1228	0.0031	100	0.9771	0.9636	0.9469	0.9263	3.0769
0.2716	12.0	900	0.1310	0.0031	100	0.9790	0.9661	0.9505	0.9301	2.5175
0.2716	13.0	975	0.1295	0.0031	100	0.9778	0.9648	0.9493	0.9296	2.7972
0.0167	14.0	1050	0.1349	0.0031	100	0.9766	0.9627	0.9461	0.9247	2.8671
0.0167	15.0	1125	0.1347	0.0031	100	0.9784	0.9643	0.9476	0.9266	2.8671
0.0167	16.0	1200	0.1330	0.0031	100	0.9772	0.9629	0.9455	0.9237	2.8671
0.0167	17.0	1275	0.1476	0.0031	100	0.9765	0.9624	0.9456	0.9244	3.0769
0.0167	18.0	1350	0.1475	0.0031	100	0.9754	0.9611	0.9441	0.9223	2.9371
0.0167	19.0	1425	0.1467	0.0031	100	0.9796	0.9660	0.9498	0.9290	2.4476
0.0061	20.0	1500	0.1371	0.0031	100	0.9778	0.9640	0.9468	0.9247	2.9371
0.0061	21.0	1575	0.1331	0.0031	100	0.9784	0.9647	0.9482	0.9271	2.7273
0.0061	22.0	1650	0.1366	0.0031	100	0.9771	0.9627	0.9449	0.9219	2.8671
0.0061	23.0	1725	0.1247	0.0031	100	0.9808	0.9684	0.9532	0.9337	2.3077
0.0061	24.0	1800	0.1282	0.0031	100	0.9808	0.9688	0.9537	0.9341	2.3776
0.0061	25.0	1875	0.1286	0.0031	100	0.9814	0.9698	0.9554	0.9366	2.3077
0.0061	26.0	1950	0.1291	0.0031	100	0.9814	0.9698	0.9553	0.9365	2.3077
0.0013	27.0	2025	0.1287	0.0031	100	0.9808	0.9688	0.9537	0.9344	2.3776
0.0013	28.0	2100	0.1303	0.0031	100	0.9802	0.9678	0.9521	0.9319	2.4476
0.0013	29.0	2175	0.1308	0.0031	100	0.9802	0.9678	0.9521	0.9319	2.4476
0.0013	30.0	2250	0.1309	0.0031	100	0.9802	0.9678	0.9521	0.9319	2.4476

Framework versions

Transformers 4.51.1
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

vania2911/barto_exp1_10partition_modelo_asl3000

barto_exp1_10partition_modelo_asl3000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
barto_exp1_10partition_modelo_asl3000