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---
license: cc-by-nc-4.0
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/nllb-200-distilled-1.3B
metrics:
- bleu
- rouge
model-index:
- name: nllb-200-distilled-1.3B-ICFOSS-Malayalam_Tamil_Translation1
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-1.3B-ICFOSS-Malayalam_Tamil_Translation1
This model is a fine-tuned version of [facebook/nllb-200-distilled-1.3B](https://huggingface.co/facebook/nllb-200-distilled-1.3B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9585
- Bleu: 27.2186
- Rouge: {'rouge1': 0.24019241472720237, 'rouge2': 0.11743746052802109, 'rougeL': 0.23538895581779812, 'rougeLsum': 0.23566947893424423}
- Chrf: {'score': 61.354962127257075, '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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------:|
| 1.0887 | 1.0 | 3200 | 0.9812 | 26.4453 | {'rouge1': 0.23999162519568762, 'rouge2': 0.11750820308459373, 'rougeL': 0.2354759340604931, 'rougeLsum': 0.23574425317949493} | {'score': 60.78556495445389, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.0235 | 2.0 | 6400 | 0.9633 | 27.2057 | {'rouge1': 0.23965959444048868, 'rouge2': 0.11732010332857629, 'rougeL': 0.2348755068042092, 'rougeLsum': 0.2350956429627365} | {'score': 61.143671039500624, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.0073 | 3.0 | 9600 | 0.9592 | 27.2471 | {'rouge1': 0.24051300083618463, 'rouge2': 0.11760625620421375, 'rougeL': 0.23594757428338253, 'rougeLsum': 0.23612557860955713} | {'score': 61.30740344086827, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.0022 | 4.0 | 12800 | 0.9587 | 27.2024 | {'rouge1': 0.24037345843038344, 'rouge2': 0.1174835459617575, 'rougeL': 0.2356757544571015, 'rougeLsum': 0.23591228047430784} | {'score': 61.34302070824752, 'char_order': 6, 'word_order': 0, 'beta': 2} |
| 1.0008 | 5.0 | 16000 | 0.9585 | 27.2186 | {'rouge1': 0.24019241472720237, 'rouge2': 0.11743746052802109, 'rougeL': 0.23538895581779812, 'rougeLsum': 0.23566947893424423} | {'score': 61.354962127257075, 'char_order': 6, 'word_order': 0, 'beta': 2} |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.0 |