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---
license: cc-by-nc-4.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: NLLB-600m-swh_Latn-to-eng_Latn
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-600m-swh_Latn-to-eng_Latn
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: 1.2490
- Bleu: 31.1907
- Gen Len: 34.464
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 7
- total_train_batch_size: 14
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 2.8224 | 0.41 | 500 | 2.3121 | 8.4908 | 34.136 |
| 2.1656 | 0.83 | 1000 | 1.9451 | 14.9983 | 33.604 |
| 1.885 | 1.24 | 1500 | 1.7385 | 18.7049 | 33.928 |
| 1.6922 | 1.66 | 2000 | 1.6102 | 21.7399 | 33.648 |
| 1.5693 | 2.07 | 2500 | 1.5175 | 23.2299 | 34.912 |
| 1.4695 | 2.49 | 3000 | 1.4552 | 24.8572 | 32.612 |
| 1.4195 | 2.9 | 3500 | 1.3948 | 26.3956 | 33.56 |
| 1.3413 | 3.32 | 4000 | 1.3564 | 27.2599 | 32.824 |
| 1.3094 | 3.73 | 4500 | 1.3263 | 27.9728 | 33.42 |
| 1.2748 | 4.15 | 5000 | 1.3044 | 28.8956 | 33.56 |
| 1.227 | 4.56 | 5500 | 1.2844 | 29.8314 | 33.552 |
| 1.2255 | 4.97 | 6000 | 1.2692 | 30.4411 | 33.716 |
| 1.191 | 5.39 | 6500 | 1.2611 | 31.1336 | 34.432 |
| 1.1842 | 5.8 | 7000 | 1.2542 | 30.8819 | 33.716 |
| 1.1712 | 6.22 | 7500 | 1.2506 | 31.528 | 33.768 |
| 1.1606 | 6.63 | 8000 | 1.2490 | 31.1907 | 34.464 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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