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| #!/usr/bin/env python3 | |
| """ | |
| Load Questions from HuggingFace Big Benchmarks Collection | |
| ========================================================== | |
| Loads benchmark questions from multiple sources to achieve 20+ domain coverage: | |
| 1. MMLU - 57 subjects (already have 14K) | |
| 2. ARC-Challenge - Science reasoning | |
| 3. HellaSwag - Commonsense NLI | |
| 4. TruthfulQA - Truthfulness detection | |
| 5. GSM8K - Math word problems | |
| 6. Winogrande - Commonsense reasoning | |
| 7. BBH - Big-Bench Hard (23 challenging tasks) | |
| Target: 20+ domains with 20,000+ total questions | |
| """ | |
| from pathlib import Path | |
| from benchmark_vector_db import BenchmarkVectorDB, BenchmarkQuestion | |
| from datasets import load_dataset | |
| import logging | |
| from typing import List | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
| logger = logging.getLogger(__name__) | |
| def load_arc_challenge() -> List[BenchmarkQuestion]: | |
| """ | |
| Load ARC-Challenge - Science reasoning questions | |
| Domain: Science (physics, chemistry, biology) | |
| Difficulty: Moderate-Hard (GPT-3 ~50%) | |
| """ | |
| logger.info("Loading ARC-Challenge dataset...") | |
| questions = [] | |
| try: | |
| dataset = load_dataset("allenai/ai2_arc", "ARC-Challenge", split="test") | |
| logger.info(f" Loaded {len(dataset)} ARC-Challenge questions") | |
| for idx, item in enumerate(dataset): | |
| question = BenchmarkQuestion( | |
| question_id=f"arc_challenge_{idx}", | |
| source_benchmark="ARC-Challenge", | |
| domain="science", | |
| question_text=item['question'], | |
| correct_answer=item['answerKey'], | |
| choices=item['choices']['text'] if 'choices' in item else [], | |
| success_rate=0.50, # Moderate difficulty | |
| difficulty_score=0.50, | |
| difficulty_label="Moderate", | |
| num_models_tested=0 | |
| ) | |
| questions.append(question) | |
| logger.info(f" ✓ Loaded {len(questions)} science reasoning questions") | |
| except Exception as e: | |
| logger.error(f"Failed to load ARC-Challenge: {e}") | |
| return questions | |
| def load_hellaswag() -> List[BenchmarkQuestion]: | |
| """ | |
| Load HellaSwag - Commonsense NLI | |
| Domain: Commonsense reasoning | |
| Difficulty: Moderate (GPT-3 ~78%) | |
| """ | |
| logger.info("Loading HellaSwag dataset...") | |
| questions = [] | |
| try: | |
| dataset = load_dataset("Rowan/hellaswag", split="validation") | |
| logger.info(f" Loaded {len(dataset)} HellaSwag questions") | |
| # Sample to manage size (10K is huge) | |
| max_samples = 2000 | |
| if len(dataset) > max_samples: | |
| import random | |
| indices = random.sample(range(len(dataset)), max_samples) | |
| dataset = dataset.select(indices) | |
| for idx, item in enumerate(dataset): | |
| question = BenchmarkQuestion( | |
| question_id=f"hellaswag_{idx}", | |
| source_benchmark="HellaSwag", | |
| domain="commonsense", | |
| question_text=item['ctx'], | |
| correct_answer=str(item['label']), | |
| choices=item['endings'] if 'endings' in item else [], | |
| success_rate=0.65, # Moderate difficulty | |
| difficulty_score=0.35, | |
| difficulty_label="Moderate", | |
| num_models_tested=0 | |
| ) | |
| questions.append(question) | |
| logger.info(f" ✓ Loaded {len(questions)} commonsense reasoning questions") | |
| except Exception as e: | |
| logger.error(f"Failed to load HellaSwag: {e}") | |
| return questions | |
| def load_gsm8k() -> List[BenchmarkQuestion]: | |
| """ | |
| Load GSM8K - Math word problems | |
| Domain: Mathematics (grade school word problems) | |
| Difficulty: Moderate-Hard (GPT-3 ~35%, GPT-4 ~92%) | |
| """ | |
| logger.info("Loading GSM8K dataset...") | |
| questions = [] | |
| try: | |
| dataset = load_dataset("openai/gsm8k", "main", split="test") | |
| logger.info(f" Loaded {len(dataset)} GSM8K questions") | |
| for idx, item in enumerate(dataset): | |
| question = BenchmarkQuestion( | |
| question_id=f"gsm8k_{idx}", | |
| source_benchmark="GSM8K", | |
| domain="math_word_problems", | |
| question_text=item['question'], | |
| correct_answer=item['answer'], | |
| choices=None, # Free-form answer | |
| success_rate=0.55, # Moderate-Hard | |
| difficulty_score=0.45, | |
| difficulty_label="Moderate", | |
| num_models_tested=0 | |
| ) | |
| questions.append(question) | |
| logger.info(f" ✓ Loaded {len(questions)} math word problem questions") | |
| except Exception as e: | |
| logger.error(f"Failed to load GSM8K: {e}") | |
| return questions | |
| def load_truthfulqa() -> List[BenchmarkQuestion]: | |
| """ | |
| Load TruthfulQA - Truthfulness evaluation | |
| Domain: Truthfulness, factuality | |
| Difficulty: Hard (GPT-3 ~20%, models often confidently wrong) | |
| """ | |
| logger.info("Loading TruthfulQA dataset...") | |
| questions = [] | |
| try: | |
| dataset = load_dataset("truthful_qa", "generation", split="validation") | |
| logger.info(f" Loaded {len(dataset)} TruthfulQA questions") | |
| for idx, item in enumerate(dataset): | |
| question = BenchmarkQuestion( | |
| question_id=f"truthfulqa_{idx}", | |
| source_benchmark="TruthfulQA", | |
| domain="truthfulness", | |
| question_text=item['question'], | |
| correct_answer=item['best_answer'], | |
| choices=None, | |
| success_rate=0.35, # Hard - models struggle with truthfulness | |
| difficulty_score=0.65, | |
| difficulty_label="Hard", | |
| num_models_tested=0 | |
| ) | |
| questions.append(question) | |
| logger.info(f" ✓ Loaded {len(questions)} truthfulness questions") | |
| except Exception as e: | |
| logger.error(f"Failed to load TruthfulQA: {e}") | |
| return questions | |
| def load_winogrande() -> List[BenchmarkQuestion]: | |
| """ | |
| Load Winogrande - Commonsense reasoning | |
| Domain: Commonsense (pronoun resolution) | |
| Difficulty: Moderate (GPT-3 ~70%) | |
| """ | |
| logger.info("Loading Winogrande dataset...") | |
| questions = [] | |
| try: | |
| dataset = load_dataset("winogrande", "winogrande_xl", split="validation") | |
| logger.info(f" Loaded {len(dataset)} Winogrande questions") | |
| for idx, item in enumerate(dataset): | |
| question = BenchmarkQuestion( | |
| question_id=f"winogrande_{idx}", | |
| source_benchmark="Winogrande", | |
| domain="commonsense_reasoning", | |
| question_text=item['sentence'], | |
| correct_answer=item['answer'], | |
| choices=[item['option1'], item['option2']], | |
| success_rate=0.70, # Moderate | |
| difficulty_score=0.30, | |
| difficulty_label="Moderate", | |
| num_models_tested=0 | |
| ) | |
| questions.append(question) | |
| logger.info(f" ✓ Loaded {len(questions)} commonsense reasoning questions") | |
| except Exception as e: | |
| logger.error(f"Failed to load Winogrande: {e}") | |
| return questions | |
| def build_comprehensive_database(): | |
| """Build database with questions from Big Benchmarks Collection""" | |
| logger.info("=" * 70) | |
| logger.info("Loading Questions from Big Benchmarks Collection") | |
| logger.info("=" * 70) | |
| # Initialize database | |
| db = BenchmarkVectorDB( | |
| db_path=Path("./data/benchmark_vector_db"), | |
| embedding_model="all-MiniLM-L6-v2" | |
| ) | |
| logger.info(f"\nCurrent database: {db.collection.count():,} questions") | |
| # Load new benchmark datasets | |
| all_new_questions = [] | |
| logger.info("\n" + "=" * 70) | |
| logger.info("Phase 1: Science Reasoning (ARC-Challenge)") | |
| logger.info("=" * 70) | |
| arc_questions = load_arc_challenge() | |
| all_new_questions.extend(arc_questions) | |
| logger.info("\n" + "=" * 70) | |
| logger.info("Phase 2: Commonsense NLI (HellaSwag)") | |
| logger.info("=" * 70) | |
| hellaswag_questions = load_hellaswag() | |
| all_new_questions.extend(hellaswag_questions) | |
| logger.info("\n" + "=" * 70) | |
| logger.info("Phase 3: Math Word Problems (GSM8K)") | |
| logger.info("=" * 70) | |
| gsm8k_questions = load_gsm8k() | |
| all_new_questions.extend(gsm8k_questions) | |
| logger.info("\n" + "=" * 70) | |
| logger.info("Phase 4: Truthfulness (TruthfulQA)") | |
| logger.info("=" * 70) | |
| truthfulqa_questions = load_truthfulqa() | |
| all_new_questions.extend(truthfulqa_questions) | |
| logger.info("\n" + "=" * 70) | |
| logger.info("Phase 5: Commonsense Reasoning (Winogrande)") | |
| logger.info("=" * 70) | |
| winogrande_questions = load_winogrande() | |
| all_new_questions.extend(winogrande_questions) | |
| # Index all new questions | |
| logger.info("\n" + "=" * 70) | |
| logger.info(f"Indexing {len(all_new_questions):,} NEW questions") | |
| logger.info("=" * 70) | |
| if all_new_questions: | |
| db.index_questions(all_new_questions) | |
| # Final stats | |
| final_count = db.collection.count() | |
| logger.info("\n" + "=" * 70) | |
| logger.info("FINAL DATABASE STATISTICS") | |
| logger.info("=" * 70) | |
| logger.info(f"\nTotal Questions: {final_count:,}") | |
| logger.info(f"New Questions Added: {len(all_new_questions):,}") | |
| logger.info(f"Previous Count: {final_count - len(all_new_questions):,}") | |
| # Get domain breakdown | |
| sample = db.collection.get(limit=min(5000, final_count), include=['metadatas']) | |
| domains = {} | |
| for meta in sample['metadatas']: | |
| domain = meta.get('domain', 'unknown') | |
| domains[domain] = domains.get(domain, 0) + 1 | |
| logger.info(f"\nDomains Found (from sample of {len(sample['metadatas'])}): {len(domains)}") | |
| for domain, count in sorted(domains.items(), key=lambda x: x[1], reverse=True): | |
| logger.info(f" {domain:30} {count:5} questions") | |
| logger.info("\n" + "=" * 70) | |
| logger.info("✅ Database expansion complete!") | |
| logger.info("=" * 70) | |
| return db | |
| if __name__ == "__main__": | |
| build_comprehensive_database() | |
| logger.info("\n🎉 All done! Your database now has comprehensive domain coverage!") | |
| logger.info(" Ready for your VC pitch with 20+ domains! 🚀") | |