File size: 4,330 Bytes
c7ebaa1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | #!/usr/bin/env python3
"""
BioRLHF Model Evaluation Example
This script demonstrates how to evaluate a fine-tuned model on
biological reasoning tasks.
Usage:
python evaluate_model.py --model ./biorlhf_model --test-set kmp_test_set.json
"""
import argparse
import json
from pathlib import Path
from biorlhf import evaluate_model
def main():
"""Run model evaluation."""
parser = argparse.ArgumentParser(
description="Evaluate a fine-tuned BioRLHF model"
)
parser.add_argument(
"--model",
type=str,
required=True,
help="Path to the fine-tuned model directory",
)
parser.add_argument(
"--test-set",
type=str,
default="kmp_test_set.json",
help="Path to test questions JSON file",
)
parser.add_argument(
"--base-model",
type=str,
default="mistralai/Mistral-7B-v0.3",
help="Base model name",
)
parser.add_argument(
"--output",
type=str,
default=None,
help="Output path for detailed results JSON",
)
parser.add_argument(
"--no-quantization",
action="store_true",
help="Disable 4-bit quantization",
)
parser.add_argument(
"--temperature",
type=float,
default=0.1,
help="Generation temperature (0 for greedy)",
)
parser.add_argument(
"--max-tokens",
type=int,
default=512,
help="Maximum tokens to generate",
)
args = parser.parse_args()
# Check if test set exists
if not Path(args.test_set).exists():
print(f"Error: Test set not found at {args.test_set}")
print("\nYou can create a test set or use the default one from the data folder.")
return
print("=" * 60)
print("BioRLHF Model Evaluation")
print("=" * 60)
print(f"Model: {args.model}")
print(f"Base Model: {args.base_model}")
print(f"Test Set: {args.test_set}")
print(f"Quantization: {'Disabled' if args.no_quantization else '4-bit'}")
print("=" * 60)
# Run evaluation
results = evaluate_model(
model_path=args.model,
test_questions_path=args.test_set,
base_model=args.base_model,
use_4bit=not args.no_quantization,
max_new_tokens=args.max_tokens,
temperature=args.temperature,
)
# Print results
print("\n" + "=" * 60)
print("EVALUATION RESULTS")
print("=" * 60)
print(f"\nOverall Accuracy: {results.overall_accuracy:.1%} ({results.correct_answers}/{results.total_questions})")
print(f"\nBy Category:")
print(f" Factual: {results.factual_accuracy:.1%}")
print(f" Reasoning: {results.reasoning_accuracy:.1%}")
print(f" Calibration: {results.calibration_accuracy:.1%}")
# Show detailed results
print("\n" + "-" * 60)
print("Detailed Results:")
print("-" * 60)
for i, r in enumerate(results.detailed_results, 1):
status = "CORRECT" if r["correct"] else "WRONG"
print(f"\n{i}. [{r['category'].upper()}] {status}")
print(f" Q: {r['question'][:80]}...")
print(f" Expected: {r['expected'][:50]}..." if len(r["expected"]) > 50 else f" Expected: {r['expected']}")
print(f" Response: {r['response'][:100]}..." if len(r["response"]) > 100 else f" Response: {r['response']}")
# Save detailed results if requested
if args.output:
output_data = {
"model_path": args.model,
"base_model": args.base_model,
"test_set": args.test_set,
"metrics": {
"overall_accuracy": results.overall_accuracy,
"factual_accuracy": results.factual_accuracy,
"reasoning_accuracy": results.reasoning_accuracy,
"calibration_accuracy": results.calibration_accuracy,
"total_questions": results.total_questions,
"correct_answers": results.correct_answers,
},
"detailed_results": results.detailed_results,
}
with open(args.output, "w") as f:
json.dump(output_data, f, indent=2)
print(f"\nDetailed results saved to: {args.output}")
print("\n" + "=" * 60)
print("Evaluation complete!")
print("=" * 60)
if __name__ == "__main__":
main()
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