Base Model: Helsinki-NLP/opus-mt-id-en
Training Dataset: TEDTalks (80%) + Meeting Domain (10%)
Training Strategy: Domain adaptation with enhanced learning
Specialization: Business meetings, technical discussions, formal conversations
Training Date: 2025-05-28
Languages: Indonesian (id) → English (en)
License: Apache 2.0

🛠️ Usage

python

from transformers import MarianMTModel, MarianTokenizer

# Load model and tokenizer
model_name = "dhintech/marian-tedtalks-id-en-enhanced"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

# Translate Indonesian to English
def translate(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    outputs = model.generate(
        **inputs,
        max_length=128,
        num_beams=3,
        early_stopping=True,
        do_sample=False
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example usage
indonesian_text = "Tim marketing akan bertanggung jawab untuk strategi ini."
english_translation = translate(indonesian_text)
print(english_translation)
# Output: "The marketing team will be responsible for this strategy."

📝 Example Translations

Meeting Context Examples

Indonesian	English	Context
Selamat pagi semuanya, mari kita mulai rapat hari ini.	Good morning everyone, let's start today's meeting.	Meeting Opening
Tim marketing akan bertanggung jawab untuk strategi ini.	The marketing team will be responsible for this strategy.	Task Assignment
Database migration sudah selesai dan berjalan dengan lancar.	Database migration is complete and running smoothly.	Technical Update
Budget yang disetujui adalah 500 juta rupiah.	The approved budget is 500 million rupiah.	Financial Discussion

🎯 Intended Use Cases

Business Meeting Translation: Real-time translation during meetings
Technical Documentation: Translating technical meeting notes
Corporate Communication: Formal business correspondence
Project Management: Translating project updates and reports
Training Materials: Educational and training content translation

📊 Training Configuration

Dataset Size: 69,138 sentence pairs
TEDTalks Data: 80% of cleaned dataset
Meeting Domain Data: 10% specialized meeting content
Max Sequence Length: 128 tokens
Training Epochs: 12
Learning Rate: 1e-05
Batch Size: 12 (effective)

🔧 Technical Specifications

Model Architecture: MarianMT (Transformer-based)
Parameters: ~74M (with selective fine-tuning)
Max Input/Output Length: 128 tokens
Inference Time: ~0.12s per sentence
Memory Requirements:
- GPU: 3GB VRAM minimum
- CPU: 4GB RAM minimum

🚨 Limitations

Domain Specificity: Optimized for formal business/meeting contexts
Informal Language: May not perform optimally on very casual Indonesian
Regional Dialects: Trained primarily on standard Indonesian
Cultural Context: Some cultural nuances may be lost in translation

📚 Citation

bibtex

@misc{enhanced-marian-id-en-2025,
  title={Enhanced MarianMT Indonesian-English Translation (Meeting Domain Adaptation)},
  author={DhinTech},
  year={2025},
  publisher={Hugging Face},
  journal={Hugging Face Model Hub},
  howpublished={\url{https://huggingface.co/dhintech/marian-tedtalks-id-en-enhanced}},
  note={Enhanced with TEDTalks and meeting-specific domain adaptation}
}

🙏 Acknowledgments

Base Model: Helsinki-NLP team for the original opus-mt-id-en model
Dataset: TEDTalks corpus and custom meeting domain data
Framework: Hugging Face Transformers team

This model is specifically enhanced for Indonesian business meeting translation scenarios with domain adaptation techniques.