---
license: cc-by-nc-4.0
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/nllb-200-1.3B
metrics:
- bleu
- rouge
model-index:
- name: nllb-200-1.3B-ICFOSS-Malayalam_English_Translation1.3b
  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-200-1.3B-ICFOSS-Malayalam_English_Translation1.3b

This model is a fine-tuned version of [facebook/nllb-200-1.3B](https://huggingface.co/facebook/nllb-200-1.3B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0536
- Bleu: 36.7256
- Rouge: {'rouge1': 0.6977825292445439, 'rouge2': 0.47317224666360513, 'rougeL': 0.6369586014923634, 'rougeLsum': 0.6367120144580565}
- Chrf: {'score': 63.88643397225133, 'char_order': 6, 'word_order': 0, 'beta': 2}

## 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: 0.0002
- 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: cosine
- num_epochs: 7

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Rouge                                                                                                                        | Chrf                                                                       |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:----------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------:|
| 1.1683        | 1.0   | 5750  | 1.0774          | 35.9761 | {'rouge1': 0.6937855960659589, 'rouge2': 0.466938063654629, 'rougeL': 0.6325990208208303, 'rougeLsum': 0.6323899971616622}   | {'score': 63.363704282940446, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.1177        | 2.0   | 11500 | 1.0617          | 36.3486 | {'rouge1': 0.6957984629345982, 'rouge2': 0.47067647725021045, 'rougeL': 0.6351678391451753, 'rougeLsum': 0.6350175761315434} | {'score': 63.657728669261445, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.102         | 3.0   | 17250 | 1.0559          | 36.7216 | {'rouge1': 0.6970801919668868, 'rouge2': 0.47279660574601357, 'rougeL': 0.6364385448189633, 'rougeLsum': 0.6362592345657716} | {'score': 63.89202343434442, 'char_order': 6, 'word_order': 0, 'beta': 2}  |
| 1.0967        | 4.0   | 23000 | 1.0545          | 36.7450 | {'rouge1': 0.6977900451765099, 'rouge2': 0.4734910607221403, 'rougeL': 0.6373405033951935, 'rougeLsum': 0.6371420919202282}  | {'score': 63.918132836888965, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.0935        | 5.0   | 28750 | 1.0538          | 36.7038 | {'rouge1': 0.6978511315129863, 'rouge2': 0.4733012047244315, 'rougeL': 0.6371351829239855, 'rougeLsum': 0.6369801889854168}  | {'score': 63.87115369473548, 'char_order': 6, 'word_order': 0, 'beta': 2}  |
| 1.0928        | 6.0   | 34500 | 1.0536          | 36.7485 | {'rouge1': 0.6977169592049554, 'rouge2': 0.4734304167965041, 'rougeL': 0.636966108177003, 'rougeLsum': 0.6367749449397957}   | {'score': 63.894445637643784, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.0918        | 7.0   | 40250 | 1.0536          | 36.7256 | {'rouge1': 0.6977825292445439, 'rouge2': 0.47317224666360513, 'rougeL': 0.6369586014923634, 'rougeLsum': 0.6367120144580565} | {'score': 63.88643397225133, 'char_order': 6, 'word_order': 0, 'beta': 2}  |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1