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	0.9614	0.0034	0.0575	0.0404	0.0273	0.0150	413.5300	0.5393	0.4921	0.4484	0.3962	36.1891
No log	2.0	300	0.7098	0.0034	0.1656	0.1236	0.0875	0.0535	155.2391	0.9490	0.9150	0.8798	0.8420	6.1300
No log	3.0	450	0.7208	0.0034	0.7392	0.6461	0.5257	0.3665	33.0621	0.9504	0.9186	0.8840	0.8450	5.9823
0.4901	4.0	600	0.7311	0.0034	0.7283	0.6380	0.5091	0.3382	33.2655	0.9563	0.9269	0.8954	0.8598	5.3176
0.4901	5.0	750	0.6964	0.0034	0.7530	0.6468	0.5295	0.3664	33.1638	0.9543	0.9221	0.8876	0.8509	5.3914
0.4901	6.0	900	0.6565	0.0034	0.7484	0.6664	0.5516	0.3933	31.1292	0.9595	0.9318	0.9018	0.8675	4.8006
0.0617	7.0	1050	0.7010	0.0034	0.7308	0.6316	0.5135	0.3597	36.5209	0.9587	0.9296	0.8986	0.8636	5.0960
0.0617	8.0	1200	0.6944	0.0034	0.7498	0.6633	0.5413	0.3752	30.6205	0.9608	0.9335	0.9028	0.8668	4.6529
0.0617	9.0	1350	0.6936	0.0034	0.7514	0.6582	0.5402	0.3830	33.3672	0.9582	0.9311	0.9012	0.8686	5.0222
0.0301	10.0	1500	0.7215	0.0034	0.7439	0.6563	0.5385	0.3807	32.4517	0.9626	0.9359	0.9069	0.8753	4.5052
0.0301	11.0	1650	0.7079	0.0034	0.7598	0.6823	0.5774	0.4262	30.0102	0.9594	0.9316	0.9004	0.8657	4.9483
0.0301	12.0	1800	0.7150	0.0034	0.7616	0.6783	0.5604	0.4030	30.6205	0.9557	0.9268	0.8959	0.8614	5.2437
0.0301	13.0	1950	0.6878	0.0034	0.7558	0.6760	0.5588	0.3985	31.0275	0.9562	0.9263	0.8931	0.8577	5.3914
0.0185	14.0	2100	0.6908	0.0034	0.7564	0.6823	0.5656	0.4076	30.3154	0.9620	0.9349	0.9039	0.8694	4.5790
0.0185	15.0	2250	0.7289	0.0034	0.7666	0.6871	0.5777	0.4227	30.2136	0.9627	0.9361	0.9064	0.8734	4.5052
0.0185	16.0	2400	0.7149	0.0034	0.7602	0.6877	0.5749	0.4198	29.3998	0.9607	0.9334	0.9039	0.8707	4.6529
0.0118	17.0	2550	0.7480	0.0034	0.7504	0.6748	0.5651	0.4074	30.0102	0.9602	0.9325	0.9039	0.8714	4.6529
0.0118	18.0	2700	0.7143	0.0034	0.7706	0.6901	0.5786	0.4223	29.8067	0.9607	0.9335	0.9048	0.8722	4.6529
0.0118	19.0	2850	0.7106	0.0034	0.7609	0.6820	0.5660	0.4065	30.4171	0.9621	0.9371	0.9108	0.8816	4.4313
0.0091	20.0	3000	0.7359	0.0034	0.7469	0.6675	0.5524	0.3941	31.3327	0.9608	0.9344	0.9058	0.8742	4.5790
0.0091	21.0	3150	0.7470	0.0034	0.7387	0.6599	0.5459	0.3924	31.0275	0.9613	0.9345	0.9049	0.8717	4.6529
0.0091	22.0	3300	0.7382	0.0034	0.7455	0.6702	0.5544	0.3947	30.1119	0.9607	0.9333	0.9042	0.8723	4.7267
0.0091	23.0	3450	0.7153	0.0034	0.7442	0.6742	0.5593	0.4001	30.0102	0.9626	0.9368	0.9089	0.8783	4.4313
0.0067	24.0	3600	0.7121	0.0034	0.7486	0.6714	0.5575	0.4013	30.7223	0.9639	0.9382	0.9104	0.8801	4.3575
0.0067	25.0	3750	0.7364	0.0034	0.7464	0.6708	0.5578	0.4019	30.0102	0.9645	0.9393	0.9118	0.8819	4.2836
0.0067	26.0	3900	0.7256	0.0034	0.7516	0.6781	0.5660	0.4088	29.6033	0.9639	0.9387	0.9112	0.8812	4.3575
0.0054	27.0	4050	0.7313	0.0034	0.7474	0.6720	0.5599	0.4011	30.0102	0.9639	0.9383	0.9106	0.8804	4.3575
0.0054	28.0	4200	0.7272	0.0034	0.7529	0.6780	0.5654	0.4063	29.7050	0.9639	0.9387	0.9112	0.8812	4.3575
0.0054	29.0	4350	0.7279	0.0034	0.7528	0.6789	0.5663	0.4066	29.5015	0.9627	0.9364	0.9083	0.8777	4.5052
0.0045	30.0	4500	0.7309	0.0034	0.7530	0.6746	0.5601	0.4003	29.9084	0.9627	0.9364	0.9083	0.8777	4.5052

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

vania2911/exp4_10partition_modelo6000

exp4_10partition_modelo6000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
exp4_10partition_modelo6000