transformers tensorboard safetensors m2m_100 text2text-generation generated_from_trainer dataset:nusatranslation_mt base_model:facebook/nllb-200-distilled-600M base_model:finetune:facebook/nllb-200-distilled-600M license:cc-by-nc-4.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/nllb-200-distilled-600M
datasets:
- nusatranslation_mt
license: cc-by-nc-4.0
metrics:
- sacrebleu
tags:
- generated_from_trainer
model-index:
- name: ind-to-bbc-nmt-v6
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: nusatranslation_mt
      type: nusatranslation_mt
      config: nusatranslation_mt_btk_ind_source
      split: test
      args: nusatranslation_mt_btk_ind_source
    metrics:
    - type: sacrebleu
      value: 31.0331
      name: Sacrebleu
---

<!-- 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. -->

# ind-to-bbc-nmt-v6

This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the nusatranslation_mt dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1587
- Sacrebleu: 31.0331
- Gen Len: 45.1815

## 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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 4.3415        | 1.0   | 825  | 1.6110          | 27.0363   | 45.267  |
| 1.415         | 2.0   | 1650 | 1.2550          | 30.5956   | 45.5    |
| 1.1044        | 3.0   | 2475 | 1.1769          | 31.2342   | 45.4315 |
| 0.951         | 4.0   | 3300 | 1.1532          | 31.8633   | 45.149  |
| 0.8409        | 5.0   | 4125 | 1.1340          | 31.5171   | 45.355  |
| 0.7582        | 6.0   | 4950 | 1.1273          | 31.0686   | 45.222  |
| 0.6937        | 7.0   | 5775 | 1.1387          | 31.3129   | 45.1355 |
| 0.6433        | 8.0   | 6600 | 1.1479          | 31.444    | 45.233  |
| 0.6056        | 9.0   | 7425 | 1.1521          | 31.3122   | 45.0945 |
| 0.5819        | 10.0  | 8250 | 1.1587          | 31.0331   | 45.1815 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
