dataset_analysis.py

#!/usr/bin/env python3
"""
Comprehensive Bengali Dataset Analysis and Training Setup
Focus on available datasets and training strategies
"""

from datasets import load_dataset
import pandas as pd
import json

def analyze_available_datasets():
    """Analyze available datasets and their potential"""
    
    print("🇧🇩 BANGLI DATASET TRAINING ANALYSIS")
    print("=" * 60)
    
    # Math Dataset Analysis
    print("\n📚 AVAILABLE DATASET: MATH PROBLEMS")
    print("Dataset: hamim-87/Ashrafur_bangla_math")
    print("-" * 45)
    
    try:
        math_ds = load_dataset("hamim-87/Ashrafur_bangla_math")
        train_data = math_ds['train']
        
        print("✅ Dataset Status: READY")
        print(f"📊 Size: {len(train_data):,} examples")
        print(f"🏗️ Structure: {train_data.column_names}")
        
        # Analyze content
        problems = train_data['problem']
        solutions = train_data['solution']
        
        print("\n🔍 Content Analysis:")
        avg_problem_length = sum(len(p) for p in problems[:1000]) / min(1000, len(problems))
        avg_solution_length = sum(len(s) for s in solutions[:1000]) / min(1000, len(solutions))
        
        print(f"Average problem length: {avg_problem_length:.0f} characters")
        print(f"Average solution length: {avg_solution_length:.0f} characters")
        
        # Sample content
        print("\n📋 Sample Content:")
        sample_problem = problems[0]
        sample_solution = solutions[0]
        
        print(f"Problem: {sample_problem[:200]}...")
        print(f"Solution: {sample_solution[:200]}...")
        
        # Content types analysis
        problem_types = []
        for prob in problems[:100]:
            if 'গণিত' in prob or 'অংক' in prob:
                problem_types.append('arithmetic')
            elif 'জ্যামিতি' in prob or 'Geometry' in prob:
                problem_types.append('geometry')
            elif 'বীজগণিত' in prob or 'algebra' in prob.lower():
                problem_types.append('algebra')
            else:
                problem_types.append('general')
        
        from collections import Counter
        type_counts = Counter(problem_types)
        print(f"\nProblem types (sample): {dict(type_counts)}")
        
        return math_ds, True
        
    except Exception as e:
        print(f"❌ Error loading math dataset: {e}")
        return None, False

def analyze_gated_dataset():
    """Information about the gated plagiarism dataset"""
    
    print("\n🔒 GATED DATASET: PLAGIARISM DETECTION")
    print("Dataset: zarif98sjs/bangla-plagiarism-dataset")
    print("-" * 45)
    
    print("⚠️ Status: REQUIRES AUTHENTICATION")
    print("\n📋 To access this dataset:")
    print("1. Create Hugging Face account: https://huggingface.co/join")
    print("2. Install huggingface-cli: pip install huggingface_hub")
    print("3. Login: huggingface-cli login")
    print("4. Request access on dataset page")
    
    print("\n💡 Alternative approaches:")
    print("• Create synthetic plagiarism data")
    print("• Use other Bengali text datasets")
    print("• Focus on math dataset for now")
    print("• Build plagiarism detection from scratch")

def create_training_strategies():
    """Create comprehensive training strategies"""
    
    print("\n🎯 TRAINING STRATEGIES WITH MATH DATASET")
    print("=" * 50)
    
    strategies = [
        {
            "name": "🎓 Educational Math Assistant",
            "description": "Bengali math problem solver and tutor",
            "approach": "Fine-tune language model for step-by-step solutions",
            "applications": ["Homework help", "Test preparation", "Concept explanation"],
            "model_type": "Text Generation (T5/GPT-style)"
        },
        {
            "name": "📝 Math Problem Classifier",
            "description": "Classify math problems by type and difficulty",
            "approach": "Train classifier on problem categories",
            "applications": ["Curriculum design", "Assessment tools", "Learning paths"],
            "model_type": "Text Classification"
        },
        {
            "name": "🔍 Math Problem Generator",
            "description": "Generate new similar math problems",
            "approach": "Use training data to create variations",
            "applications": ["Practice materials", "Exam generation", "Adaptive learning"],
            "model_type": "Text Generation"
        },
        {
            "name": "💬 Conversational Math Tutor",
            "description": "Interactive math learning assistant",
            "approach": "Combine problem solving with dialogue",
            "applications": ["Personal tutoring", "24/7 help", "Student engagement"],
            "model_type": "Conversational AI"
        },
        {
            "name": "📊 Math Solution Validator",
            "description": "Verify and check math problem solutions",
            "approach": "Train on correct/incorrect solution pairs",
            "applications": ["Automated grading", "Error detection", "Quality assurance"],
            "model_type": "Binary Classification + Generation"
        }
    ]
    
    for i, strategy in enumerate(strategies, 1):
        print(f"\n{i}. {strategy['name']}")
        print(f"   📝 {strategy['description']}")
        print(f"   🔧 Approach: {strategy['approach']}")
        print(f"   🎯 Applications: {', '.join(strategy['applications'])}")
        print(f"   🤖 Model: {strategy['model_type']}")

def create_implementation_plan():
    """Create step-by-step implementation plan"""
    
    print("\n📋 IMPLEMENTATION PLAN")
    print("=" * 30)
    
    phases = [
        {
            "phase": "Phase 1: Data Preparation",
            "tasks": [
                "Load and clean math dataset",
                "Create train/validation/test splits",
                "Tokenize Bengali text",
                "Create data loaders"
            ]
        },
        {
            "phase": "Phase 2: Model Selection",
            "tasks": [
                "Choose base model (mT5, mGPT, or custom)",
                "Set up model architecture",
                "Configure training parameters",
                "Initialize tokenizer"
            ]
        },
        {
            "phase": "Phase 3: Training Setup",
            "tasks": [
                "Set up training environment",
                "Configure GPU/CPU training",
                "Set up logging and monitoring",
                "Prepare evaluation metrics"
            ]
        },
        {
            "phase": "Phase 4: Model Training",
            "tasks": [
                "Start training with small sample",
                "Monitor loss and metrics",
                "Adjust hyperparameters",
                "Train on full dataset"
            ]
        },
        {
            "phase": "Phase 5: Evaluation & Deployment",
            "tasks": [
                "Evaluate on test set",
                "Generate sample outputs",
                "Create inference pipeline",
                "Deploy model"
            ]
        }
    ]
    
    for phase in phases:
        print(f"\n🎯 {phase['phase']}")
        for task in phase['tasks']:
            print(f"   • {task}")

def create_code_templates():
    """Create ready-to-use code templates"""
    
    print("\n💻 READY-TO-USE CODE TEMPLATES")
    print("=" * 40)
    
    print("\n1. 📚 Data Loading Template:")
    template1 = '''
from datasets import load_dataset
from transformers import AutoTokenizer

# Load dataset
ds = load_dataset("hamim-87/Ashrafur_bangla_math")
train_data = ds['train']

# Initialize tokenizer (Bengali-compatible)
tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")

# Prepare data
def prepare_data(examples):
    inputs = [f"প্রশ্ন: {q}" for q in examples['problem']]
    targets = examples['solution']
    
    model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding=True)
    labels = tokenizer(targets, max_length=512, truncation=True, padding=True)
    
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

train_dataset = train_data.map(prepare_data, batched=True)
'''
    print(template1)
    
    print("\n2. 🤖 Training Template:")
    template2 = '''
from transformers import AutoModelForSeq2SeqLM, TrainingArguments, Trainer

# Initialize model
model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small")

# Training arguments
training_args = TrainingArguments(
    output_dir="./bangla_math_model",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    evaluation_strategy="steps",
    eval_steps=1000,
    save_steps=1000,
)

# Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

# Train
trainer.train()
'''
    print(template2)

def main():
    """Main function"""
    
    # Analyze available datasets
    math_ds, success = analyze_available_datasets()
    
    if success:
        # Show gated dataset info
        analyze_gated_dataset()
        
        # Create training strategies
        create_training_strategies()
        
        # Implementation plan
        create_implementation_plan()
        
        # Code templates
        create_code_templates()
        
        print("\n🎉 READY TO START TRAINING!")
        print("Choose your preferred strategy and let's begin!")
    else:
        print("❌ Dataset loading failed. Check your connection.")

if __name__ == "__main__":
    main()