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Eyesiga/
opus-mt-en-lg-finetuned-en-to-lg-finetuned-en-to-lm

Name: opus-mt-en-lg-finetuned-en-to-lg-finetuned-en-to-lm
Availability: InStock

generated_from_trainer

Tobius/opus-mt-en-lg-finetuned-en-to-lg

finetune

endpoints_compatible

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opus-mt-en-lg-finetuned-en-to-lg-finetuned-en-to-lm

This model is a fine-tuned version of Tobius/opus-mt-en-lg-finetuned-en-to-lg on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7639
Bleu: 30.9462
Gen Len: 21.6305

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
No log	1.0	146	3.6537	0.5489	30.8399
No log	2.0	292	3.0866	0.5298	22.7759
No log	3.0	438	2.7528	0.8364	26.5419
3.8771	4.0	584	2.4983	1.6885	25.3966
3.8771	5.0	730	2.2893	2.265	23.0296
3.8771	6.0	876	2.1164	3.5594	21.9409
2.5379	7.0	1022	1.9606	3.8191	22.8325
2.5379	8.0	1168	1.8145	4.7176	22.5025
2.5379	9.0	1314	1.6974	5.5036	21.7931
2.5379	10.0	1460	1.5895	7.0622	21.9483
2.0088	11.0	1606	1.4934	7.9064	22.2143
2.0088	12.0	1752	1.4120	8.7831	20.9754
2.0088	13.0	1898	1.3244	10.7815	21.899
1.6503	14.0	2044	1.2544	11.9959	21.835
1.6503	15.0	2190	1.1843	12.9721	21.766
1.6503	16.0	2336	1.1271	15.3546	21.5542
1.6503	17.0	2482	1.0670	15.9538	21.7906
1.3905	18.0	2628	1.0184	19.1229	21.9212
1.3905	19.0	2774	0.9797	20.3025	21.0148
1.3905	20.0	2920	0.9358	22.3064	21.6946
1.1903	21.0	3066	0.9012	24.9192	21.3128
1.1903	22.0	3212	0.8704	25.8138	21.3202
1.1903	23.0	3358	0.8481	26.8542	21.5591
1.0573	24.0	3504	0.8228	28.4838	21.3251
1.0573	25.0	3650	0.8069	29.1448	21.399
1.0573	26.0	3796	0.7892	29.8567	21.5985
1.0573	27.0	3942	0.7777	29.8862	21.6404
0.9649	28.0	4088	0.7703	30.9483	21.7315
0.9649	29.0	4234	0.7660	30.9831	21.6108
0.9649	30.0	4380	0.7639	30.9462	21.6305

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Eyesiga/opus-mt-en-lg-finetuned-en-to-lg-finetuned-en-to-lm

opus-mt-en-lg-finetuned-en-to-lg-finetuned-en-to-lm

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Eyesiga/
opus-mt-en-lg-finetuned-en-to-lg-finetuned-en-to-lm