helsinki-nlp-model-training

name	helsinki-nlp-model-training
description	Fine-tune Helsinki-NLP OPUS-MT models for Chuukese translation with custom training data, evaluation, and deployment. Use when training, evaluating, fine-tuning, or deploying translation models for Chuukese-English translation. Use when this capability is needed.
metadata	{"author":"findinfinitelabs"}

Helsinki-NLP Model Training

Overview

Train and fine-tune Helsinki-NLP OPUS-MT models for Chuukese-English translation. These models are specifically designed for translation tasks and perform better than general LLMs for low-resource languages like Chuukese.

Base Models:

• Helsinki-NLP/opus-mt-mul-en (Multilingual → English)
• Helsinki-NLP/opus-mt-en-mul (English → Multilingual)

Capabilities

• Fine-tuning: Adapt base models to Chuukese-specific data
• Bidirectional Training: Support for both Chuukese→English and English→Chuukese
• Training Data Preparation: Format dictionary and parallel corpus data
• Model Evaluation: BLEU, chrF scoring on test sets
• Local Deployment: Run models locally without API calls
• GPU/CPU Support: Automatic device detection and optimization

Model Architecture

Helsinki-NLP OPUS-MT Architecture:
┌─────────────────────────────────────────────────┐
│  MarianMT (Marian Neural Machine Translation)   │
├─────────────────────────────────────────────────┤
│  Encoder: 6 Transformer layers                  │
│  Decoder: 6 Transformer layers                  │
│  Attention heads: 8                             │
│  Hidden size: 512                               │
│  Vocabulary: SentencePiece tokenizer            │
└─────────────────────────────────────────────────┘

Directory Structure

models/
├── helsinki-chuukese_chuukese_to_english/
│   ├── finetuned/
│   │   ├── config.json
│   │   ├── pytorch_model.bin
│   │   ├── tokenizer_config.json
│   │   ├── source.spm
│   │   └── target.spm
│   └── training_logs/
├── helsinki-chuukese_english_to_chuukese/
│   ├── finetuned/
│   └── training_logs/
└── test-helsinki_chuukese_to_english/

Training Data Preparation

1. Data Format

# Training data structure
training_pairs = [
    {"source": "chomong", "target": "to help, assist"},
    {"source": "Kopwe pwan chomong", "target": "We will help"},
    {"source": "ngang", "target": "fish"},
]

# Export to TSV format
def export_training_data(pairs, output_path, direction="chk_to_en"):
    """Export training pairs to TSV format for transformers."""
    with open(output_path, 'w', encoding='utf-8') as f:
        for pair in pairs:
            if direction == "chk_to_en":
                f.write(f"{pair['source']}\t{pair['target']}\n")
            else:
                f.write(f"{pair['target']}\t{pair['source']}\n")

2. Data Sources

# Combine multiple data sources
from src.database.dictionary_db import DictionaryDB

def prepare_training_data():
    """Prepare comprehensive training dataset."""
    db = DictionaryDB()
    
    training_pairs = []
    
    # 1. Dictionary entries
    entries = db.dictionary.find({'is_base_word': True})
    for entry in entries:
        training_pairs.append({
            'source': entry['chuukese_word'],
            'target': entry['english_definition'],
            'confidence': 0.9
        })
    
    # 2. Phrases
    phrases = db.phrases.find({'confidence_score': {'$gte': 0.7}})
    for phrase in phrases:
        training_pairs.append({
            'source': phrase['chuukese_phrase'],
            'target': phrase['english_translation'],
            'confidence': phrase.get('confidence_score', 0.8)
        })
    
    # 3. Bible parallel verses (highest quality)
    from src.utils.nwt_epub_parser import NWTEpubParser
    # ... extract parallel verses
    
    return training_pairs

3. Data Splitting

from sklearn.model_selection import train_test_split

def split_training_data(pairs, test_size=0.1, val_size=0.1):
    """Split data into train/val/test sets."""
    train, test = train_test_split(pairs, test_size=test_size, random_state=42)
    train, val = train_test_split(train, test_size=val_size/(1-test_size), random_state=42)
    
    return {
        'train': train,
        'val': val,
        'test': test
    }

Training Implementation

HelsinkiChuukeseTranslator Class

import torch
from transformers import (
    AutoTokenizer,
    AutoModelForSeq2SeqLM,
    Trainer,
    TrainingArguments,
    DataCollatorForSeq2Seq
)
from datasets import Dataset

class HelsinkiChuukeseTranslator:
    """Helsinki-NLP based Chuukese translator with fine-tuning support."""
    
    def __init__(self,
                 base_model: str = "Helsinki-NLP/opus-mt-mul-en",
                 reverse_model: str = "Helsinki-NLP/opus-mt-en-mul"):
        self.base_model = base_model
        self.reverse_model = reverse_model
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        
        # Local fine-tuned model paths
        self.local_chk_to_en = "models/helsinki-chuukese_chuukese_to_english/finetuned"
        self.local_en_to_chk = "models/helsinki-chuukese_english_to_chuukese/finetuned"
        
        self.tokenizer = None
        self.model = None
        
    def setup_models(self, direction: str = "chk_to_en") -> bool:
        """Load models, preferring fine-tuned versions."""
        try:
            if direction == "chk_to_en":
                model_path = .local_chk_to_en  os.path.exists(.local_chk_to_en)  .base_model
            :
                model_path = .local_en_to_chk  os.path.exists(.local_en_to_chk)  .reverse_model
            
            .tokenizer = AutoTokenizer.from_pretrained(model_path)
            .model = AutoModelForSeq2SeqLM.from_pretrained(model_path).to(.device)
            
             
         Exception  e:
            ()
             
    
     () -> :
        
         .model  :
            .setup_models(direction)
        
        inputs = .tokenizer(text, return_tensors=, padding=).to(.device)
        
        outputs = .model.generate(
            **inputs,
            max_length=,
            num_beams=,
            early_stopping=
        )
        
         .tokenizer.decode(outputs[], skip_special_tokens=)

Fine-Tuning Script

def fine_tune_model(
    training_data: list,
    direction: str = "chk_to_en",
    epochs: int = 10,
    batch_size: int = 16,
    learning_rate: float = 2e-5,
    output_dir: str = None
):
    """
    Fine-tune Helsinki-NLP model on Chuukese data.
    
    Args:
        training_data: List of {'source': str, 'target': str} pairs
        direction: 'chk_to_en' or 'en_to_chk'
        epochs: Number of training epochs
        batch_size: Training batch size
        learning_rate: Learning rate
        output_dir: Directory to save fine-tuned model
    """
    # Select base model
    if direction == "chk_to_en":
        base_model = "Helsinki-NLP/opus-mt-mul-en"
        output_dir = output_dir or "models/helsinki-chuukese_chuukese_to_english/finetuned"
    else:
        base_model = "Helsinki-NLP/opus-mt-en-mul"
        output_dir = output_dir or "models/helsinki-chuukese_english_to_chuukese/finetuned"
    
    # Load tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained(base_model)
    model = AutoModelForSeq2SeqLM.from_pretrained(base_model)
    
    # Prepare dataset
    def preprocess_function(examples):
        inputs = examples['source']
        targets = examples['target']
        
        model_inputs = tokenizer(inputs, max_length=128, truncation=True, padding='max_length')
        
        with tokenizer.as_target_tokenizer():
            labels = tokenizer(targets, max_length=128, truncation=, padding=)
        
        model_inputs[] = labels[]
         model_inputs
    
    
    dataset = Dataset.from_dict({
        : [p[]  p  training_data],
        : [p[]  p  training_data]
    })
    
    tokenized_dataset = dataset.(preprocess_function, batched=)
    
    
    split = tokenized_dataset.train_test_split(test_size=)
    
    
    training_args = TrainingArguments(
        output_dir=output_dir,
        num_train_epochs=epochs,
        per_device_train_batch_size=batch_size,
        per_device_eval_batch_size=batch_size,
        learning_rate=learning_rate,
        weight_decay=,
        evaluation_strategy=,
        save_strategy=,
        load_best_model_at_end=,
        logging_dir=,
        logging_steps=,
        fp16=torch.cuda.is_available(),  
        gradient_accumulation_steps=,
        warmup_ratio=,
    )
    
    
    data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
    
    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=split[],
        eval_dataset=split[],
        data_collator=data_collator,
    )
    
    
    trainer.train()
    
    
    trainer.save_model(output_dir)
    tokenizer.save_pretrained(output_dir)
    
    ()
    
     trainer

Model Evaluation

import sacrebleu

def evaluate_model(translator, test_pairs: list, direction: str = "chk_to_en"):
    """Evaluate translation model on test set."""
    predictions = []
    references = []
    
    for pair in test_pairs:
        source = pair['source']
        target = pair['target']
        
        prediction = translator.translate(source, direction)
        predictions.append(prediction)
        references.append([target])  # sacrebleu expects list of references
    
    # Calculate BLEU
    bleu = sacrebleu.corpus_bleu(predictions, references)
    
    # Calculate chrF (preferred for low-resource languages)
    chrf = sacrebleu.corpus_chrf(predictions, references)
    
    return {
        'bleu': bleu.score,
        'chrf': chrf.score,
        'num_samples': len(test_pairs),
        'sample_predictions': list(zip(
            [p['source'] for p in test_pairs[:5]],
            [p['target'] for p in test_pairs[:5]],
            predictions[:5]
        ))
    }

GPU vs CPU Training

def get_device_config():
    """Get optimal device configuration for training."""
    if torch.cuda.is_available():
        device = "cuda"
        gpu_name = torch.cuda.get_device_name(0)
        gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1e9
        
        # Adjust batch size based on GPU memory
        if gpu_memory >= 16:
            batch_size = 32
        elif gpu_memory >= 8:
            batch_size = 16
        else:
            batch_size = 8
        
        return {
            'device': device,
            'gpu_name': gpu_name,
            'gpu_memory_gb': gpu_memory,
            'batch_size': batch_size,
            'fp16': True,
            'gradient_accumulation': 2
        }
    else:
        return {
            'device': 'cpu',
            'batch_size': 4,
            'fp16': False,
            'gradient_accumulation': 8
        }

Usage Examples

Basic Training

# Prepare data
training_data = prepare_training_data()
splits = split_training_data(training_data)

# Train Chuukese → English model
fine_tune_model(
    training_data=splits['train'],
    direction="chk_to_en",
    epochs=10
)

# Train English → Chuukese model
fine_tune_model(
    training_data=splits['train'],
    direction="en_to_chk",
    epochs=10
)

Model Comparison

# Compare base vs fine-tuned
translator = HelsinkiChuukeseTranslator()

# Test with base model
translator.setup_models(use_finetuned=False)
base_results = evaluate_model(translator, splits['test'])

# Test with fine-tuned model
translator.setup_models(use_finetuned=True)
finetuned_results = evaluate_model(translator, splits['test'])

print(f"Base BLEU: {base_results['bleu']:.2f}")
print(f"Fine-tuned BLEU: {finetuned_results['bleu']:.2f}")
print(f"Improvement: {finetuned_results['bleu'] - base_results['bleu']:.2f}")

Best Practices

Training

1. Use high-quality data: Bible verses and validated dictionary entries
2. Balance dataset: Mix words, phrases, and sentences
3. Early stopping: Monitor validation loss to prevent overfitting
4. Mixed precision: Use FP16 on GPU for faster training
5. Gradient accumulation: Use for larger effective batch sizes on limited memory

Evaluation

1. Use chrF for low-resource languages: More stable than BLEU
2. Human evaluation: Periodic review by native speakers
3. Back-translation: Verify round-trip consistency
4. Cultural validation: Ensure cultural concepts are preserved

Deployment

1. Quantization: Use 8-bit quantization for faster inference
2. Caching: Cache tokenizer and model loading
3. Batch processing: Process multiple translations together
4. Fallback: Keep base model as fallback if fine-tuned fails

Dependencies

• torch>=2.0.0: PyTorch
• transformers>=4.57.0: Hugging Face Transformers
• datasets>=2.0.0: Hugging Face Datasets
• sentencepiece>=0.1.99: Tokenization
• sacrebleu>=2.0.0: Evaluation metrics
• accelerate>=0.20.0: Training acceleration

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