---
library_name: pytorch
pipeline_tag: text2text-generation
language:
- vi
- lo
metrics:
- bleu
---

## Direct Use

Please use python version 3.10

### Load a pre-trained model

Use `load_config` to load a .yaml config file.

Then use `load_model_tokenizer` to load a pretrained model and its tokenizers
```
from config import load_config
from load_model import load_model_tokenizer

config = load_config(file_name='config/config_final.yaml')
model, src_tokenizer, tgt_tokenizer = load_model_tokenizer(config)
```

### Translate lo to vi
Use the `translate` function in `translate.py`.
```
from translate import translate
from config import load_config
from load_model import load_model_tokenizer

config = load_config(file_name='config/config_final.yaml')
model, src_tokenizer, tgt_tokenizer = load_model_tokenizer(config)

text = "   "
translation, attn = translate(
    model, src_tokenizer, tgt_tokenizer, text,
    decode_method='beam-search',
)
print(translation)
```

## Training
Use the `train_model` function in `train.py` to train your model.
```
from train import train_model
from config import load_config

config = load_config(file_name='config/config_final.yaml')
train_model(config)
```

If you wish to continue training/ fine-tune our model, you should 
modify the `num_epochs` in your desired config file,
as well as read the following notes (`+` is the string concat funtion):
- The code will save and preload models in `model_folder`
- The code will preload the model with the name: "`model_basename` + `preload` + `.pt`"
- The code will NOT preload a trained model if you set `preload` as `null`
- Every epoch, the code will save the model with the name: "`model_basename` + `_` + (current epoch) + `.pt`"
- `train_model` will automatically continue training the `preload`ed model.