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---
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_Hindi_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-malayalam_Hindi_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: 0.3788
- Bleu: 62.5154
- Rouge: {'rouge1': 0.42504662037099206, 'rouge2': 0.2891987093258279, 'rougeL': 0.4211514655126128, 'rougeLsum': 0.42156526904087943}
- Chrf: {'score': 79.24933104383702, '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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------:|
| 0.5095 | 1.0 | 4698 | 0.4099 | 59.5376 | {'rouge1': 0.4220305313233426, 'rouge2': 0.2866519629954242, 'rougeL': 0.41646494668344247, 'rougeLsum': 0.4167340351207185} | {'score': 77.52631821685847, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 0.4213 | 2.0 | 9396 | 0.3842 | 61.7541 | {'rouge1': 0.4247871478803683, 'rouge2': 0.28898946927686797, 'rougeL': 0.42099815319030365, 'rougeLsum': 0.4209781732451786} | {'score': 78.54007352748269, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 0.3888 | 3.0 | 14094 | 0.3785 | 62.2691 | {'rouge1': 0.42665978089706913, 'rouge2': 0.28916951694997156, 'rougeL': 0.42136280849134333, 'rougeLsum': 0.4219221144613403} | {'score': 79.11003191466068, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 0.3764 | 4.0 | 18792 | 0.3785 | 62.4514 | {'rouge1': 0.42373682879235186, 'rouge2': 0.2891987093258279, 'rougeL': 0.41970156954196886, 'rougeLsum': 0.4201735443294585} | {'score': 79.20088697777769, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 0.3741 | 5.0 | 23490 | 0.3788 | 62.5154 | {'rouge1': 0.42504662037099206, 'rouge2': 0.2891987093258279, 'rougeL': 0.4211514655126128, 'rougeLsum': 0.42156526904087943} | {'score': 79.24933104383702, '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 |