Create score_model.py

score_model.py

@@ -0,0 +1,19 @@
+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