| from sentence_transformers import SentenceTransformer, util | |
| def evaluate_model(model_name,dataset): | |
| try: | |
| model = SentenceTransformer(model_name) | |
| scores = [] | |
| for row in dataset: | |
| emb1 = model.encode(row["instruction"], convert_to_tensor=True) | |
| emb2 = model.encode(row["output"], convert_to_tensor=True) | |
| sim_score = float(util.cos_sim(emb1, emb2)[0]) | |
| scores.append(sim_score) | |
| return sum(scores) / len(scores) | |
| except Exception as e: | |
| print(f"Evaluation failed: {e}") | |
| return None |