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Safe: no meaningful spoiler detectedMild: broad setup, tone, or non-critical plot informationMajor: key twist, death, identity, ending, solution, or final outcome revealedsklearn.svm.SVC)sentence-transformers/all-mpnet-base-v2Major, Mild, or Safebest_model.joblib. It contains both the trained classifier and metadata with the embedding model name and label classes.| Model | Accuracy | Macro F1 | Weighted F1 |
|---|---|---|---|
| SVM RBF | 0.5753 | 0.5723 | 0.5752 |
| Logistic Regression | 0.5669 | 0.5706 | 0.5661 |
| MLP | 0.5690 | 0.5640 | 0.5670 |
| Random Forest | 0.5314 | 0.4166 | 0.4434 |
1import joblib
2from sentence_transformers import SentenceTransformer
3
4payload = joblib.load("best_model.joblib")
5model = payload["model"]
6metadata = payload["metadata"]
7classes = metadata["label_classes"]
8
9embedder = SentenceTransformer(metadata["embedding_model"])
10text = "The final scene reveals that the detective was the killer all along."
11X = embedder.encode([text], convert_to_numpy=True, normalize_embeddings=True)
12label_id = int(model.predict(X)[0])
13print(classes[label_id])