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-v7
  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: 31.4148
      name: Sacrebleu
---

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# ind-to-bbc-nmt-v7

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: 1.1534
- Sacrebleu: 31.4148
- Gen Len: 45.246

## 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: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 5.1799        | 1.0   | 413  | 2.3351          | 25.3863   | 45.489  |
| 1.6805        | 2.0   | 826  | 1.3384          | 30.3818   | 45.661  |
| 1.2114        | 3.0   | 1239 | 1.2202          | 30.9982   | 45.562  |
| 1.0517        | 4.0   | 1652 | 1.1827          | 31.2905   | 45.3925 |
| 0.9461        | 5.0   | 2065 | 1.1678          | 31.6094   | 45.2625 |
| 0.8728        | 6.0   | 2478 | 1.1471          | 31.2517   | 45.4265 |
| 0.8153        | 7.0   | 2891 | 1.1497          | 31.332    | 45.1645 |
| 0.7719        | 8.0   | 3304 | 1.1467          | 31.372    | 45.3915 |
| 0.743         | 9.0   | 3717 | 1.1491          | 31.4979   | 45.0825 |
| 0.7204        | 10.0  | 4130 | 1.1534          | 31.4148   | 45.246  |


### Framework versions

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