nllb-200-distilled-600M-Mal-Tami

AlakaDeepak/nllb-200-distilled-600M-Mal-Tami

nllb-200-distilled-600M-Mal-Tami is a machine learning model from AlakaDeepak. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as cc-by-nc-4.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: transformers
License: cc-by-nc-4.0
Model type: m2m_100
Access: Public
Created: Dec 28, 2023
Updated: Dec 28, 2023
SHA: a324a475

Model card

From the Hugging Face model README

nllb-200-distilled-600M-Mal-Tami

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: 0.7611
Bleu: 37.9665
Rouge: {'rouge1': 0.32743825959084827, 'rouge2': 0.185409074130288, 'rougeL': 0.32502232667423403, 'rougeLsum': 0.32586316574736196}

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Rouge
1.1388	1.0	734	0.8947	33.7987	{'rouge1': 0.32849704791121687, 'rouge2': 0.18672956891173587, 'rougeL': 0.32647539033778816, 'rougeLsum': 0.32688430438771043}
0.898	2.0	1468	0.8281	35.3540	{'rouge1': 0.32674790770839823, 'rouge2': 0.18650128486750617, 'rougeL': 0.32482214840866075, 'rougeLsum': 0.32547267295223703}
0.8046	3.0	2202	0.7962	36.1416	{'rouge1': 0.32674790770839823, 'rouge2': 0.1857352651103779, 'rougeL': 0.3244104763689233, 'rougeLsum': 0.3251467537911681}
0.7448	4.0	2936	0.7805	36.7560	{'rouge1': 0.32674790770839823, 'rouge2': 0.1858005621530716, 'rougeL': 0.32443967060792683, 'rougeLsum': 0.3251613509106698}
0.6985	5.0	3670	0.7696	37.3576	{'rouge1': 0.32614806855270073, 'rouge2': 0.18527678135494344, 'rougeL': 0.32362185574038427, 'rougeLsum': 0.3242525469535007}
0.663	6.0	4404	0.7661	37.8431	{'rouge1': 0.3275909830405743, 'rouge2': 0.18575262957184815, 'rougeL': 0.32552891023599473, 'rougeLsum': 0.32626719518749486}
0.636	7.0	5138	0.7639	37.8764	{'rouge1': 0.32740041520695484, 'rouge2': 0.18505379438127528, 'rougeL': 0.3249628569281158, 'rougeLsum': 0.3257682636769831}
0.6176	8.0	5872	0.7606	37.9621	{'rouge1': 0.3274220405691797, 'rouge2': 0.18538283587780377, 'rougeL': 0.3249736696092282, 'rougeLsum': 0.3257829175063507}
0.6047	9.0	6606	0.7605	37.9538	{'rouge1': 0.32743825959084827, 'rouge2': 0.185409074130288, 'rougeL': 0.32502232667423403, 'rougeLsum': 0.32586316574736196}
0.5969	10.0	7340	0.7611	37.9665	{'rouge1': 0.32743825959084827, 'rouge2': 0.185409074130288, 'rougeL': 0.32502232667423403, 'rougeLsum': 0.32586316574736196}

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.16.0
Tokenizers 0.13.3

Library: transformers
Type: m2m_100
License: cc-by-nc-4.0
Created: Dec 28, 2023
Updated: Dec 28, 2023