"""Run SawBench evaluation against an OpenAI-compatible API endpoint. Usage: # OpenAI export OPENAI_API_KEY="sk-..." python run_eval_openai.py --model gpt-4o-mini # Any OpenAI-compatible endpoint (vLLM, Ollama, Together, etc.) python run_eval_openai.py \ --base-url http://localhost:8000/v1 \ --model my-model \ --api-key dummy # Specific tasks + limited items python run_eval_openai.py --model gpt-4o-mini --tasks spell,reverse --max-items 20 """ import argparse, json, os, re, sys, time from pathlib import Path from collections import defaultdict from datasets import load_dataset def load_items(tasks=None, max_items=None): ds = load_dataset("omneity-labs/spellbench", split="test") items = list(ds) if tasks: items = [item for item in items if item["task"] in tasks] if max_items: by_task = defaultdict(list) for item in items: by_task[item["task"]].append(item) items = [] for task_items in by_task.values(): items.extend(task_items[:max_items]) return items def load_prompts(data_dir): with open(data_dir / "prompts.json") as f: return json.load(f) def format_prompt(item, prompt_templates): task = item["task"] tmpl_info = prompt_templates.get(task) if not tmpl_info: return f"Task: {task}\nInput: {item['input']}" tmpl = tmpl_info["prompts"][0] input_val = item["input"] if " | " in input_val: parts = input_val.split(" | ") main_input = parts[0] params = {} for p in parts[1:]: if ": " in p: k, v = p.split(": ", 1) params[k.strip()] = v.strip() fmt = {"input": main_input, "word": main_input, "sentence": main_input} fmt.update(params) if "position" in params: fmt["n"] = params["position"] if "word number" in params: fmt["n"] = params["word number"] for p in parts[1:]: m = re.match(r"word (\d+) vs word (\d+)", p.strip()) if m: fmt["n"] = m.group(1) fmt["m"] = m.group(2) else: fmt = {"input": input_val, "word": input_val, "sentence": input_val} try: return tmpl.format(**fmt) except KeyError: return f"Task: {task}\nInput: {item['input']}" def extract_answer(response, task): response = response.strip() json_match = re.search(r'\{[^}]+\}', response) if json_match: try: data = json.loads(json_match.group()) if "answer" in data: return str(data["answer"]) if "result" in data: return str(data["result"]) except json.JSONDecodeError: pass if task in ("word_length", "vowel_count", "consonant_count", "count_letter", "word_count", "total_letters", "nth_word_length"): nums = re.findall(r'\b(\d+)\b', response) if nums: return nums[-1] if task in ("is_palindrome", "contains_letter"): lower = response.lower() if "true" in lower or "yes" in lower: return "true" if "false" in lower or "no" in lower: return "false" if task == "compare_lengths": lower = response.lower() if "equal" in lower: return "equal" if "first" in lower: return "first" if "second" in lower: return "second" if task in ("first_letter", "last_letter", "nth_letter"): m = re.search(r"['\"](.)['\"]", response) if m: return m.group(1) m = re.search(r'\b([a-zA-Z])\b', response) if m: return m.group(1) lines = response.strip().split("\n") return lines[-1].strip() def check_answer(predicted, expected, task): predicted = str(predicted).strip().lower() expected = str(expected).strip().lower() if task in ("unique_letters", "double_letters", "all_first_letters", "letter_positions"): pred_norm = re.sub(r'\s*,\s*', ', ', predicted) exp_norm = re.sub(r'\s*,\s*', ', ', expected) return pred_norm == exp_norm return predicted == expected def call_api(client, model, prompt, max_tokens=128, temperature=0): """Call the chat completions API.""" response = client.chat.completions.create( model=model, messages=[{"role": "user", "content": prompt}], max_tokens=max_tokens, temperature=temperature, ) return response.choices[0].message.content def run_eval(client, model, data_dir, tasks=None, max_items=None, max_tokens=128, delay=0): prompts_data = load_prompts(data_dir) items = load_items(tasks, max_items) print(f"Loaded {len(items)} items across {len(set(i['task'] for i in items))} tasks") results = [] task_scores = defaultdict(lambda: {"correct": 0, "total": 0}) t0 = time.time() for i, item in enumerate(items): prompt_text = format_prompt(item, prompts_data) try: response = call_api(client, model, prompt_text, max_tokens) except Exception as e: print(f" Error on {item['id']}: {e}") response = "" predicted = extract_answer(response, item["task"]) correct = check_answer(predicted, item["expected"], item["task"]) task_scores[item["task"]]["total"] += 1 if correct: task_scores[item["task"]]["correct"] += 1 results.append({ "id": item["id"], "task": item["task"], "input": item["input"], "expected": item["expected"], "predicted": predicted, "raw_response": response, "correct": correct, "metadata": item["metadata"] }) if (i + 1) % 50 == 0: elapsed = time.time() - t0 print(f" [{i+1}/{len(items)}] {elapsed:.1f}s elapsed") if delay > 0: time.sleep(delay) elapsed = time.time() - t0 summary = { "model": model, "total_items": len(items), "total_correct": sum(1 for r in results if r["correct"]), "accuracy": sum(1 for r in results if r["correct"]) / len(items) if items else 0, "elapsed_seconds": round(elapsed, 1), "per_task": {}, } for task, scores in sorted(task_scores.items()): acc = scores["correct"] / scores["total"] if scores["total"] else 0 summary["per_task"][task] = { "correct": scores["correct"], "total": scores["total"], "accuracy": round(acc, 4), } return summary, results def main(): parser = argparse.ArgumentParser(description="Run SawBench evaluation via OpenAI API") parser.add_argument("--model", required=True, help="Model name") parser.add_argument("--base-url", default=None, help="API base URL (default: OpenAI)") parser.add_argument("--api-key", default=None, help="API key (default: OPENAI_API_KEY env var)") parser.add_argument("--data-dir", default=str(Path(__file__).parent / "data")) parser.add_argument("--tasks", default=None, help="Comma-separated task names (default: all)") parser.add_argument("--max-items", type=int, default=None, help="Max items per task") parser.add_argument("--max-tokens", type=int, default=128) parser.add_argument("--delay", type=float, default=0, help="Delay between requests in seconds (rate limiting)") parser.add_argument("--output", default="results.json") args = parser.parse_args() try: from openai import OpenAI except ImportError: print("Please install the openai package: uv pip install openai") sys.exit(1) api_key = args.api_key or os.environ.get("OPENAI_API_KEY") if not api_key and not args.base_url: print("Set OPENAI_API_KEY or provide --api-key and --base-url") sys.exit(1) client_kwargs = {} if args.base_url: client_kwargs["base_url"] = args.base_url if api_key: client_kwargs["api_key"] = api_key client = OpenAI(**client_kwargs) tasks = args.tasks.split(",") if args.tasks else None data_dir = Path(args.data_dir) summary, results = run_eval( client, args.model, data_dir, tasks, args.max_items, args.max_tokens, args.delay, ) print(f"\n{'='*60}") print(f"Model: {summary['model']}") print(f"Overall: {summary['total_correct']}/{summary['total_items']} " f"({summary['accuracy']:.1%})") print(f"Time: {summary['elapsed_seconds']}s") print(f"{'='*60}") print(f"{'Task':<25} {'Correct':>8} {'Total':>6} {'Accuracy':>9}") print(f"{'-'*25} {'-'*8} {'-'*6} {'-'*9}") for task, info in summary["per_task"].items(): print(f"{task:<25} {info['correct']:>8} {info['total']:>6} {info['accuracy']:>8.1%}") output_path = Path(args.output) with open(output_path, "w") as f: json.dump({"summary": summary, "results": results}, f, indent=2, ensure_ascii=False) print(f"\nDetailed results saved to {output_path}") if __name__ == "__main__": main()