Create script.py

script.py

@@ -0,0 +1,22 @@
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.metrics.pairwise import cosine_similarity
+
+test_df = pd.read_csv("/tmp/data/test.csv")
+
+with open("model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+scores = []
+for _, row in test_df.iterrows():
+    X_query = model["tokenizer"].transform([row["Query"]])
+    is_cand = sum([(model["faq_ids"] == row[f"FAQ{i+1}"]).astype(int) for i in range(3)]) > 0
+    sim = cosine_similarity(X_query, model["X_faq"][is_cand])[0]
+    score = sim.max()
+    scores.append(score)
+
+predict = (np.array(scores) > model["thr"]).astype(int)
+
+df = pd.DataFrame([(f"testid{i:04}", v) for i, v in enumerate(predict)], columns=["id", "pred"])
+df.to_csv("submission.csv", index=None)