Training Framework: PyTorch + Transformers
Training Data: TED Talks parallel corpus
Dataset Usage: 50% of full dataset
Training Parameters:
- Learning Rate: 3e-5
- Batch Size: 4/2 (GPU/CPU)
- Max Length: 128 tokens
- Epochs: 5

Usage

python

from transformers import MarianMTModel, MarianTokenizer

# Load model and tokenizer
tokenizer = MarianTokenizer.from_pretrained("dhintech/marian-id-en-meeting-translation")
model = MarianMTModel.from_pretrained("dhintech/marian-id-en-meeting-translation")

# Translate Indonesian to English
def translate(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True)
    outputs = model.generate(**inputs, max_length=128, num_beams=4)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example usage
indonesian_text = "Selamat pagi, terima kasih sudah datang."
english_translation = translate(indonesian_text)
print(english_translation)

Example Translations

Indonesian	English
Selamat pagi, terima kasih sudah datang.	Good morning, thank you for coming.
Teknologi AI berkembang sangat pesat.	AI technology is developing very rapidly.
Mari kita diskusikan hasil penelitian ini.	Let's discuss the results of this research.

Performance

Optimized for conversational and presentation-style text
Best performance on formal Indonesian text
Model size: approximately 300MB
Suitable for mobile deployment

Citation

bibtex

@misc{marian-id-en-meeting-translation,
  title={MarianMT Indonesian-English Translation (Fine-tuned)},
  author={DhinTech},
  year={2025},
  publisher={Hugging Face},
  journal={Hugging Face Model Hub},
  howpublished={\url{https://huggingface.co/dhintech/marian-id-en-meeting-translation}}
}