tst-gun-gub-pt / README.md
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---
language:
- multilingual
- pt
base_model: /content/opus-mt-en-mul
tags:
- generated_from_trainer
datasets:
- tiagoblima/translation-pt-indigenouns
metrics:
- bleu
model-index:
- name: tst-gun-gub-pt
results:
- task:
name: Translation
type: translation
dataset:
name: tiagoblima/translation-pt-indigenouns
type: tiagoblima/translation-pt-indigenouns
metrics:
- name: Bleu
type: bleu
value: 8.5368
---
<!-- 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. -->
# tst-gun-gub-pt
This model is a fine-tuned version of [/content/opus-mt-en-mul](https://huggingface.co//content/opus-mt-en-mul) on the tiagoblima/translation-pt-indigenouns dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8158
- Bleu: 8.5368
- Gen Len: 59.24
## 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
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 2.4421 | 0.29 | 4000 | 2.5285 | 3.3785 | 68.48 |
| 2.1667 | 0.59 | 8000 | 2.3018 | 4.5883 | 58.6 |
| 2.0255 | 0.88 | 12000 | 2.1290 | 5.1052 | 67.3 |
| 1.8995 | 1.18 | 16000 | 2.0535 | 7.8429 | 55.48 |
| 1.8322 | 1.47 | 20000 | 1.9960 | 7.2663 | 58.24 |
| 1.7868 | 1.77 | 24000 | 1.9224 | 7.0981 | 66.34 |
| 1.7012 | 2.06 | 28000 | 1.8869 | 7.5657 | 60.3 |
| 1.6773 | 2.36 | 32000 | 1.8613 | 7.9888 | 61.18 |
| 1.6631 | 2.65 | 36000 | 1.8354 | 8.0862 | 60.5 |
| 1.6379 | 2.94 | 40000 | 1.8158 | 8.4077 | 60.18 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1