gqa-opus-mt-de-en

This model is a fine-tuned version of sbartlett97/gqa-opus-mt-de-en on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.6134
• Bleu: 0.2252
• Gen Len: 13.0925

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 25
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
1.5403	1.0	62500	1.9670	0.2318	12.8925
1.5017	2.0	125000	1.9568	0.2285	12.8835
1.4314	3.0	187500	1.9667	0.2227	12.863
1.4056	4.0	250000	1.9781	0.2306	12.9815
1.3433	5.0	312500	2.0043	0.2312	12.937
1.3228	6.0	375000	2.0210	0.2283	12.9465
1.2224	7.0	437500	2.0480	0.2284	12.9285
1.2103	8.0	500000	2.0813	0.2305	12.901
1.1647	9.0	562500	2.1105	0.2288	12.93
1.1148	10.0	625000	2.1348	0.2289	12.989
1.1014	11.0	687500	2.1448	0.2282	12.924
1.0690	12.0	750000	2.1797	0.2258	12.9675
0.9994	13.0	812500	2.2147	0.2272	12.996
0.9748	14.0	875000	2.2385	0.2251	12.9535
0.9504	15.0	937500	2.2863	0.224	12.975
0.9147	16.0	1000000	2.3162	0.2241	12.9705
0.8565	17.0	1062500	2.3561	0.2272	13.012
0.8204	18.0	1125000	2.3846	0.2273	13.0055
0.7785	19.0	1187500	2.4334	0.2217	12.991
0.7603	20.0	1250000	2.4639	0.2237	13.0475
0.7153	21.0	1312500	2.5014	0.2213	13.051
0.6761	22.0	1375000	2.5300	0.2216	13.0385
0.6352	23.0	1437500	2.5624	0.2219	13.078
0.6079	24.0	1500000	2.5957	0.2245	13.087
0.5723	25.0	1562500	2.6134	0.2252	13.0925

Framework versions

• Transformers 5.0.0.dev0
• Pytorch 2.9.1+cu128
• Datasets 4.4.1
• Tokenizers 0.22.1