---
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-Hindi_Malayalam_Translator
  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-Hindi_Malayalam_Translator

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: 0.5883
- Bleu: 32.3133
- Rouge: {'rouge1': 0.4065168353303946, 'rouge2': 0.2762287202150305, 'rougeL': 0.3947284265080875, 'rougeLsum': 0.3952044100349186}
- Chrf: {'score': 69.00192526889488, '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: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Rouge                                                                                                                        | Chrf                                                                      |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:----------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|
| 0.7199        | 1.0   | 4698  | 0.6215          | 29.8169 | {'rouge1': 0.405762448982788, 'rouge2': 0.2722973168668976, 'rougeL': 0.3920717141056125, 'rougeLsum': 0.39226265285587325}  | {'score': 67.671645233609, 'char_order': 6, 'word_order': 0, 'beta': 2}   |
| 0.6269        | 2.0   | 9396  | 0.5960          | 31.5169 | {'rouge1': 0.4074341383663418, 'rouge2': 0.2754310444010575, 'rougeL': 0.3950826699767377, 'rougeLsum': 0.3954069337543914}  | {'score': 68.50668767972792, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 0.5962        | 3.0   | 14094 | 0.5891          | 32.4131 | {'rouge1': 0.4065168353303946, 'rouge2': 0.2774409260882534, 'rougeL': 0.3949096085748628, 'rougeLsum': 0.39546625690693493} | {'score': 68.94783702655978, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 0.5855        | 4.0   | 18792 | 0.5882          | 32.4648 | {'rouge1': 0.4065168353303946, 'rouge2': 0.2762287202150305, 'rougeL': 0.3947284265080875, 'rougeLsum': 0.3952044100349186}  | {'score': 69.10087499970177, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 0.5835        | 5.0   | 23490 | 0.5883          | 32.3133 | {'rouge1': 0.4065168353303946, 'rouge2': 0.2762287202150305, 'rougeL': 0.3947284265080875, 'rougeLsum': 0.3952044100349186}  | {'score': 69.00192526889488, '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