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	0.4939	0.0035	0.7940	0.7431	0.6246	0.4697	22.9908	0.9380	0.9009	0.8632	0.8244	7.4242
No log	2.0	300	0.4169	0.0035	0.8179	0.7459	0.6315	0.4817	24.2116	0.9568	0.9283	0.8975	0.8634	5.2273
No log	3.0	450	0.3977	0.0035	0.8199	0.7461	0.6370	0.4995	22.8891	0.9582	0.9306	0.9002	0.8657	5.0758
0.4335	4.0	600	0.4259	0.0035	0.8071	0.7533	0.6364	0.4776	22.9908	0.9627	0.9365	0.9081	0.8754	4.4697
0.4335	5.0	750	0.3683	0.0035	0.8599	0.8031	0.6959	0.5524	18.2096	0.9665	0.9431	0.9171	0.8866	3.8636
0.4335	6.0	900	0.3889	0.0035	0.8316	0.7665	0.6575	0.5022	21.1597	0.9640	0.9401	0.9136	0.8832	4.3182
0.0653	7.0	1050	0.4314	0.0035	0.8489	0.7888	0.6864	0.5501	20.3459	0.9666	0.9437	0.9177	0.8879	3.7879
0.0653	8.0	1200	0.4212	0.0035	0.8579	0.7999	0.6957	0.5523	18.7182	0.9653	0.9414	0.9154	0.8855	4.2424
0.0653	9.0	1350	0.4298	0.0035	0.8530	0.7917	0.6886	0.5460	19.4303	0.9628	0.9387	0.9111	0.8800	4.0909
0.0374	10.0	1500	0.4524	0.0035	0.8184	0.7516	0.6436	0.5061	23.8047	0.9684	0.9457	0.9206	0.8919	3.7121
0.0374	11.0	1650	0.4429	0.0035	0.8392	0.7797	0.6771	0.5381	22.2787	0.9640	0.9395	0.9129	0.8818	4.1667
0.0374	12.0	1800	0.4376	0.0035	0.8479	0.7939	0.6902	0.5468	19.4303	0.9640	0.9403	0.9137	0.8828	4.2424
0.0374	13.0	1950	0.4262	0.0035	0.8447	0.7834	0.6851	0.5424	20.7528	0.9653	0.9426	0.9169	0.8883	4.0152
0.0233	14.0	2100	0.4827	0.0035	0.8478	0.7877	0.6793	0.5323	19.9390	0.9666	0.9445	0.9186	0.8881	3.7879
0.0233	15.0	2250	0.4489	0.0035	0.8459	0.7872	0.6792	0.5276	19.9390	0.9653	0.9434	0.9184	0.8899	3.9394
0.0233	16.0	2400	0.4538	0.0035	0.8579	0.7989	0.6915	0.5404	18.7182	0.9652	0.9421	0.9154	0.8843	3.9394
0.0152	17.0	2550	0.5196	0.0035	0.8339	0.7799	0.6687	0.5179	19.6338	0.9703	0.9483	0.9234	0.8939	3.3333
0.0152	18.0	2700	0.4844	0.0035	0.8416	0.7862	0.6851	0.5429	19.6338	0.9665	0.9440	0.9192	0.8897	3.8636
0.0152	19.0	2850	0.4768	0.0035	0.8498	0.7884	0.6839	0.5380	20.2442	0.9665	0.9436	0.9171	0.8856	3.9394
0.0111	20.0	3000	0.4795	0.0035	0.8599	0.8037	0.7016	0.5574	18.5148	0.9652	0.9413	0.9142	0.8823	4.0909
0.0111	21.0	3150	0.4967	0.0035	0.8400	0.7771	0.6712	0.5224	20.3459	0.9690	0.9467	0.9211	0.8912	3.7121
0.0111	22.0	3300	0.4832	0.0035	0.8518	0.7905	0.6817	0.5365	18.9217	0.9678	0.9449	0.9189	0.8882	3.7879
0.0111	23.0	3450	0.5077	0.0035	0.8479	0.7921	0.6842	0.5358	18.9217	0.9665	0.9431	0.9169	0.8858	3.8636
0.0067	24.0	3600	0.4972	0.0035	0.8569	0.7994	0.6973	0.5553	18.6165	0.9672	0.9447	0.9190	0.8885	3.7879
0.0067	25.0	3750	0.5022	0.0035	0.8529	0.7989	0.6962	0.5480	18.5148	0.9678	0.9454	0.9200	0.8903	3.7121
0.0067	26.0	3900	0.5223	0.0035	0.8509	0.7967	0.6938	0.5480	18.5148	0.9653	0.9426	0.9162	0.8850	3.9394
0.006	27.0	4050	0.5225	0.0035	0.8498	0.7977	0.6946	0.5482	18.4130	0.9671	0.9450	0.9194	0.8894	3.7879
0.006	28.0	4200	0.5249	0.0035	0.8539	0.8001	0.6969	0.5500	18.2096	0.9678	0.9449	0.9187	0.8877	3.7879
0.006	29.0	4350	0.5314	0.0035	0.8469	0.7906	0.6845	0.5375	18.9217	0.9678	0.9449	0.9187	0.8877	3.7879
0.0044	30.0	4500	0.5327	0.0035	0.8489	0.7929	0.6865	0.5401	18.8199	0.9678	0.9449	0.9187	0.8877	3.7879

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

vania2911/barto_exp4_10partition_modelo_msl6000

barto_exp4_10partition_modelo_msl6000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

vania2911/
barto_exp4_10partition_modelo_msl6000