NLLB-600m-swh_Latn-to-eng_Latn – AI Model by Shamus | AlphaNeural AI

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NLLB-600m-swh_Latn-to-eng_Latn

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NLLB-600m-swh_Latn-to-eng_Latn

This model is a fine-tuned version of facebook/nllb-200-distilled-600M on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2490
Bleu: 31.1907
Gen Len: 34.464

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: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 7
total_train_batch_size: 14
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 8000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
2.8224	0.41	500	2.3121	8.4908	34.136
2.1656	0.83	1000	1.9451	14.9983	33.604
1.885	1.24	1500	1.7385	18.7049	33.928
1.6922	1.66	2000	1.6102	21.7399	33.648
1.5693	2.07	2500	1.5175	23.2299	34.912
1.4695	2.49	3000	1.4552	24.8572	32.612
1.4195	2.9	3500	1.3948	26.3956	33.56
1.3413	3.32	4000	1.3564	27.2599	32.824
1.3094	3.73	4500	1.3263	27.9728	33.42
1.2748	4.15	5000	1.3044	28.8956	33.56
1.227	4.56	5500	1.2844	29.8314	33.552
1.2255	4.97	6000	1.2692	30.4411	33.716
1.191	5.39	6500	1.2611	31.1336	34.432
1.1842	5.8	7000	1.2542	30.8819	33.716
1.1712	6.22	7500	1.2506	31.528	33.768
1.1606	6.63	8000	1.2490	31.1907	34.464

Framework versions

Transformers 4.21.3
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1