Model Card for mbart-large-50-mmt-ko-vi

This model is fine-tuned from mBART-large-50 using multilingual translation data of Korean legal documents for Korean-to-Vietnamese translation tasks.

---

Table of Contents

• Model Card for mbart-large-50-mmt-ko-vi
• Table of Contents
• Model Details
  • Model Description
• Uses
  • Direct Use
  • Out-of-Scope Use
• Bias, Risks, and Limitations
• Training Details
  • Training Data
  • Training Procedure
    • Preprocessing
    • Speeds, Sizes, Times
• Evaluation
  • Testing Data
  • Metrics
  • Results
• Environmental Impact
• Technical Specifications
• Citation
• Model Card Contact

---

Model Details

Model Description

• Developed by: Jaeyoon Myoung, Heewon Kwak
• Shared by: ofu
• Model type: Language model (Translation)
• Language(s) (NLP): Korean, Vietnamese
• License: Apache 2.0
• Parent Model: facebook/mbart-large-50-many-to-many-mmt

---

Uses

Direct Use

This model is used for text translation from Korean to Vietnamese.

Out-of-Scope Use

This model is not suitable for translation tasks involving languages other than Korean.

---

Bias, Risks, and Limitations

The model may contain biases inherited from the training data and may produce inappropriate translations for sensitive topics.

---

Training Details

Training Data

The model was trained using multilingual translation data of Korean legal documents provided by AI Hub.

Training Procedure

Preprocessing

• Removed unnecessary whitespace, special characters, and line breaks.

Speeds, Sizes, Times

• Training Time: 1 hour 25 minutes (5,100 seconds) on Nvidia RTX 4090
• Throughput: ~3.51 samples/second
• Total Training Samples: 17,922
• Model Checkpoint Size: Approximately 2.3GB
• Gradient Accumulation Steps: 4
• FP16 Mixed Precision Enabled: Yes

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 8 (per device)
• eval_batch_size: 8 (per device)
• seed: 42
• distributed_type: single-node (since _n_gpu=1 and no distributed training setup is indicated)
• num_devices: 1 (single NVIDIA GPU: RTX 4090)
• gradient_accumulation_steps: 4
• total_train_batch_size: 32 (calculated as train_batch_size * gradient_accumulation_steps)
• total_eval_batch_size: 8 (evaluation does not use gradient accumulation)
• optimizer: AdamW (indicated by optim=OptimizerNames.ADAMW_TORCH)
• lr_scheduler_type: linear (indicated by lr_scheduler_type=SchedulerType.LINEAR)
• lr_scheduler_warmup_steps: 100
• num_epochs: 3

---

Evaluation

Testing Data

The evaluation used a dataset partially extracted from Korean labor law precedents.

Metrics

• BLEU

Results

• BLEU Score: 29.69
• Accuracy: 95.65%

---

Environmental Impact

• Hardware Type: NVIDIA RTX 4090
• Power Consumption: ~450W
• Training Time: 1 hour 25 minutes (1.42 hours)
• Electricity Consumption: ~0.639 kWh
• Carbon Emission Factor (South Korea): 0.459 kgCO₂/kWh
• Estimated Carbon Emissions: ~0.293 kgCO₂

---

Technical Specifications

• Model Architecture: Based on mBART-large-50, a multilingual sequence-to-sequence transformer model designed for translation tasks. The architecture includes 24 encoder and 24 decoder layers with 1,024 hidden units.

• Software:

  • sacrebleu for evaluation
  • Hugging Face Transformers library for fine-tuning
  • Python 3.11.9 and PyTorch 2.4.0

• Hardware: NVIDIA RTX 4090 with 24GB VRAM was used for training and inference.

• Tokenization and Preprocessing: The tokenization was performed using the SentencePiece model pre-trained with mBART-large-50. Text preprocessing included removing special characters, unnecessary whitespace, and normalizing line breaks.

---

Citation

Currently, there are no papers or blog posts available for this model.

---

Model Card Contact

• Contact Email: audwodbs492@ofu.co.kr | gguldanzi@ofu.co.kr