YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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 preloaded model.

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