trinhhuy/finetuned-opus-mt-en-vi

This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-vi on harouzie/vi_en-translation dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.1596
• Validation Loss: 0.2877
• Epoch: 3
• Bleu: 77.71982942978268

Model description

🧪 Quick Start

First, make sure to install the transformers library:

pip install transformers

Option 1: Use a pipeline as a high-level helper

from transformers import pipeline

pipe = pipeline("translation", model="trinhhuy/finetuned-opus-mt-en-vi")

result = pipe("I'm confident that my friend will pass the exam because he has been studying hard and staying focused for weeks.")
print(result)

[{'translation_text': 'Tôi tự tin rằng bạn tôi sẽ vượt qua kỳ thi vì anh ấy đã học tập chăm chỉ và tập trung nhiều tuần.'}]

Option 2: Load model directly

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("trinhhuy/finetuned-opus-mt-en-vi")
model = AutoModelForSeq2SeqLM.from_pretrained("trinhhuy/finetuned-opus-mt-en-vi")

input_tokenized = tokenizer("I'm confident that my friend will pass the exam because he has been studying hard and staying focused for weeks.", return_tensors="pt")
output_tokenized = model.generate(**input_tokenized)

translated_text = tokenizer.decode(output_tokenized[0], skip_special_tokens=True)
print(translated_text)

Tôi tự tin rằng bạn tôi sẽ vượt qua kỳ thi vì anh ấy đã học tập chăm chỉ và tập trung nhiều tuần.

Training hyperparameters

The following hyperparameters were used during training:

• initial_learning_rate = 5e-05
• weight_decay_rate = 0.01
• training_precision: mixed_float16

Training results

Train Loss	Validation Loss	Epoch
0.4246	0.3235	0
0.2724	0.2977	1
0.2019	0.2887	2
0.1596	0.2877	3

Framework versions

• Transformers 4.53.3
• TensorFlow 2.18.0
• Datasets 4.0.0
• Tokenizers 0.21.2