transformers pytorch tensorboard m2m_100 text2text-generation generated_from_trainer license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible region:us
---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- bleu
- rouge
model-index:
- name: nllb-200-distilled-600M-Mal-Tami
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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

This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/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
