---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/nllb-200-distilled-600M
tags:
- generated_from_trainer
model-index:
- name: nllb-indo-en-cleaned
  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-indo-en-cleaned

This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3048

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| No log        | 1.0     | 96   | 4.3318          |
| 53.1274       | 2.0     | 192  | 2.0475          |
| 25.7634       | 3.0     | 288  | 0.4936          |
| 8.4388        | 4.0     | 384  | 0.2444          |
| 1.7896        | 5.0     | 480  | 0.2407          |
| 0.8853        | 6.0     | 576  | 0.2626          |
| 0.5583        | 7.0     | 672  | 0.2793          |
| 0.4353        | 8.0     | 768  | 0.2936          |
| 0.3497        | 9.0     | 864  | 0.2992          |
| 0.2969        | 10.0    | 960  | 0.3038          |
| 0.2713        | 10.4199 | 1000 | 0.3048          |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0