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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 |