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| """
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| Benchmark Evaluation Script for Model Text Similarity
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| =========================================================
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| Compares generated results with reference texts using text embeddings.
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| """
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| import numpy as np
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| import requests
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| import subprocess
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| import os
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| from pathlib import Path
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| from typing import List
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| MODEL_NAME = "grok-4.1-fast"
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| MODEL_RESULTS_PATH = "/path/to/your/model_generate_results_dir/%s/" % MODEL_NAME
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| TEST_DATA_PATH = "/path/to/your/DATA_PATH/test/"
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| class TextSimilarityCalculator:
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| def __init__(self, model_name="bge-m3", ollama_host="http://localhost:11434"):
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| self.model_name = model_name
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| self.ollama_host = ollama_host
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| def get_embedding(self, text: str) -> List[float]:
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| try:
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| response = requests.get(f"{self.ollama_host}/api/tags")
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| if response.status_code != 200:
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| return None
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| payload = {"model": self.model_name, "prompt": text, "stream": False}
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| response = requests.post(f"{self.ollama_host}/api/embeddings", json=payload, timeout=30)
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| if response.status_code == 200:
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| return response.json().get("embedding", [])
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| return None
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| except:
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| return None
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| def cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
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| if not vec1 or not vec2:
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| return 0.0
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| vec1, vec2 = np.array(vec1), np.array(vec2)
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| norm1, norm2 = np.linalg.norm(vec1), np.linalg.norm(vec2)
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| if norm1 == 0 or norm2 == 0:
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| return 0.0
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| return np.dot(vec1, vec2) / (norm1 * norm2)
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| def calculate_similarity(self, text1: str, text2: str) -> float:
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| embedding1, embedding2 = self.get_embedding(text1), self.get_embedding(text2)
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| if embedding1 is None or embedding2 is None:
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| return 0.0
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| return float(self.cosine_similarity(embedding1, embedding2))
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|
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| def check_ollama_installation(self):
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| try:
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| result = subprocess.run(["ollama", "--version"], capture_output=True, text=True)
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| if result.returncode == 0:
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| result = subprocess.run(["ollama", "list"], capture_output=True, text=True)
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| return self.model_name in result.stdout
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| return False
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| except:
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| return False
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| def find_matching_txt_files(ref_dir, test_dir):
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| matches = []
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| ref_txt_files = list(Path(ref_dir).glob("*.txt"))
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| for txt_path in Path(test_dir).rglob("*.txt"):
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| txt_name = txt_path.name
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| matching_ref = [ref for ref in ref_txt_files if ref.name == txt_name]
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| if matching_ref:
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| for ref_file in matching_ref:
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| matches.append((ref_file, txt_path))
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| return matches
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| def read_file_content(file_path):
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| try:
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| with open(file_path, 'r', encoding='utf-8') as f:
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| return f.read().strip()
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| except:
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| return ""
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| def main():
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| matches = find_matching_txt_files(MODEL_RESULTS_PATH, TEST_DATA_PATH)
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| if not matches:
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| print("No matching txt files found")
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| return
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| print(f"Found {len(matches)} matching txt file pairs")
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| print("-" * 50)
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| calculator = TextSimilarityCalculator()
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| if not calculator.check_ollama_installation():
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| print("Ollama environment check failed")
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| return
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| similarities = []
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| for i, (ref_path, test_path) in enumerate(matches, 1):
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| ref_content = read_file_content(ref_path)
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| test_content = read_file_content(test_path)
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| if not ref_content or not test_content:
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| print(f"File {ref_path.name}: Skipped (empty content)")
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| continue
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| similarity = calculator.calculate_similarity(ref_content, test_content)
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| similarities.append(similarity)
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|
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| print(f"Pair {i}: {ref_path.name}")
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| print(f" Reference file: {ref_path}")
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| print(f" Target file: {test_path}")
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| print(f" Similarity: {similarity:.4f}")
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| print("-" * 30)
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| if similarities:
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| avg_similarity = np.mean(similarities)
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| print("=" * 50)
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| print(f"Total file pairs: {len(similarities)}")
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| print(f"Average similarity: {avg_similarity:.4f}")
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| else:
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| print("No valid file pairs for similarity calculation")
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| if __name__ == "__main__":
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| main()
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|
