pytorch tensorboard t5 text2text-generation dataset:mt_eng_vietnamese transformers generated_from_trainer model-index autotrain_compatible
---
tags:
- generated_from_trainer
datasets:
- mt_eng_vietnamese
metrics:
- bleu
model-index:
- name: t5vi-finetuned-en-to-vi
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: mt_eng_vietnamese
      type: mt_eng_vietnamese
      args: iwslt2015-en-vi
    metrics:
    - name: Bleu
      type: bleu
      value: 13.547
---

<!-- 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. -->

# t5vi-finetuned-en-to-vi

This model is a fine-tuned version of [imthanhlv/t5vi](https://huggingface.co/imthanhlv/t5vi) on the mt_eng_vietnamese dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3827
- Bleu: 13.547
- Gen Len: 17.3719

## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.8026        | 1.0   | 6666  | 1.5907          | 10.9756 | 17.3231 |
| 1.6217        | 2.0   | 13332 | 1.4635          | 12.375  | 17.3444 |
| 1.5087        | 3.0   | 19998 | 1.4131          | 13.1828 | 17.3924 |
| 1.4446        | 4.0   | 26664 | 1.3915          | 13.5217 | 17.3617 |
| 1.4076        | 5.0   | 33330 | 1.3827          | 13.547  | 17.3719 |


### Framework versions

- Transformers 4.19.1
- Pytorch 1.11.0+cu113
- Datasets 2.2.1
- Tokenizers 0.12.1
