---
library_name: transformers
language:
- nan
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-ZH
tags:
- generated_from_trainer
datasets:
- sarahwei/Taiwanese-Minnan-Example-Sentences
metrics:
- bleu
model-index:
- name: helsinki_new_ver1
  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. -->

# helsinki_new_ver1

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ZH](https://huggingface.co/Helsinki-NLP/opus-mt-en-ZH) on the sarahwei/Taiwanese-Minnan-Example-Sentences dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4153
- Bleu: 0.0081

## 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: 1e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 20000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Bleu   |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 0.8615        | 0.5656  | 1000  | 0.5171          | 0.0028 |
| 0.809         | 1.1312  | 2000  | 0.4937          | 0.0035 |
| 0.802         | 1.6968  | 3000  | 0.4801          | 0.0038 |
| 0.763         | 2.2624  | 4000  | 0.4691          | 0.0052 |
| 0.7475        | 2.8281  | 5000  | 0.4607          | 0.0053 |
| 0.7363        | 3.3937  | 6000  | 0.4534          | 0.0057 |
| 0.7263        | 3.9593  | 7000  | 0.4470          | 0.0057 |
| 0.7166        | 4.5249  | 8000  | 0.4413          | 0.0062 |
| 0.7119        | 5.0905  | 9000  | 0.4363          | 0.0061 |
| 0.7097        | 5.6561  | 10000 | 0.4324          | 0.0064 |
| 0.6921        | 6.2217  | 11000 | 0.4289          | 0.0070 |
| 0.692         | 6.7873  | 12000 | 0.4258          | 0.0064 |
| 0.6773        | 7.3529  | 13000 | 0.4232          | 0.0067 |
| 0.6918        | 7.9186  | 14000 | 0.4210          | 0.0073 |
| 0.6789        | 8.4842  | 15000 | 0.4194          | 0.0078 |
| 0.6801        | 9.0498  | 16000 | 0.4178          | 0.0072 |
| 0.6734        | 9.6154  | 17000 | 0.4167          | 0.0070 |
| 0.6808        | 10.1810 | 18000 | 0.4159          | 0.0079 |
| 0.669         | 10.7466 | 19000 | 0.4155          | 0.0081 |
| 0.6667        | 11.3122 | 20000 | 0.4153          | 0.0081 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1