---
license: cc-by-4.0
base_model: Helsinki-NLP/opus-mt-tc-big-en-pt
tags:
- generated_from_trainer
datasets:
- kde4
model-index:
- name: opus-en-to-pt-translate
  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. -->

# opus-en-to-pt-translate

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tc-big-en-pt](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-pt) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5618

## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4982        | 0.08  | 500   | 0.6398          |
| 0.5475        | 0.15  | 1000  | 0.6370          |
| 0.5397        | 0.23  | 1500  | 0.6333          |
| 0.5267        | 0.31  | 2000  | 0.6272          |
| 0.5212        | 0.39  | 2500  | 0.6240          |
| 0.522         | 0.46  | 3000  | 0.6179          |
| 0.5213        | 0.54  | 3500  | 0.6124          |
| 0.5155        | 0.62  | 4000  | 0.6114          |
| 0.5143        | 0.7   | 4500  | 0.6053          |
| 0.5037        | 0.77  | 5000  | 0.6058          |
| 0.5093        | 0.85  | 5500  | 0.6002          |
| 0.5253        | 0.93  | 6000  | 0.5945          |
| 0.5138        | 1.01  | 6500  | 0.5892          |
| 0.4864        | 1.08  | 7000  | 0.5906          |
| 0.491         | 1.16  | 7500  | 0.5889          |
| 0.4993        | 1.24  | 8000  | 0.5849          |
| 0.4749        | 1.32  | 8500  | 0.5849          |
| 0.4911        | 1.39  | 9000  | 0.5812          |
| 0.487         | 1.47  | 9500  | 0.5796          |
| 0.4846        | 1.55  | 10000 | 0.5758          |
| 0.4863        | 1.63  | 10500 | 0.5739          |
| 0.4792        | 1.7   | 11000 | 0.5725          |
| 0.4816        | 1.78  | 11500 | 0.5704          |
| 0.4811        | 1.86  | 12000 | 0.5684          |
| 0.4773        | 1.94  | 12500 | 0.5676          |
| 0.4657        | 2.01  | 13000 | 0.5691          |
| 0.4246        | 2.09  | 13500 | 0.5683          |
| 0.4285        | 2.17  | 14000 | 0.5693          |
| 0.4241        | 2.25  | 14500 | 0.5676          |
| 0.422         | 2.32  | 15000 | 0.5669          |
| 0.4199        | 2.4   | 15500 | 0.5656          |
| 0.4273        | 2.48  | 16000 | 0.5650          |
| 0.4161        | 2.56  | 16500 | 0.5651          |
| 0.4243        | 2.63  | 17000 | 0.5635          |
| 0.4202        | 2.71  | 17500 | 0.5628          |
| 0.4152        | 2.79  | 18000 | 0.5627          |
| 0.4179        | 2.87  | 18500 | 0.5619          |
| 0.4241        | 2.94  | 19000 | 0.5618          |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2