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from sentence_transformers import SentenceTransformer, util
MODELS = {
"all-MiniLM-L6-v2": SentenceTransformer("all-MiniLM-L6-v2"),
"multi-qa-MiniLM-L6-cos-v1": SentenceTransformer("multi-qa-MiniLM-L6-cos-v1"),
"paraphrase-MiniLM-L3-v2": SentenceTransformer("paraphrase-MiniLM-L3-v2"),
"all-mpnet-base-v2": SentenceTransformer("all-mpnet-base-v2"),
"distilbert-base-nli-mean-tokens": SentenceTransformer("distilbert-base-nli-mean-tokens"),
}
def score_fit(text: str, goal: str, method: str) -> dict:
results = {}
for name, model in MODELS.items():
emb1 = model.encode(text, convert_to_tensor=True)
emb2 = model.encode(goal, convert_to_tensor=True)
cos = util.cos_sim(emb1, emb2).item()
score = max(0, min(100, int((cos + 1) * 50)))
results[name] = score
return results
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