using Logistic Regression and similar to the example provided

authorship-verification-submission/.dockerignore

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+predictions.jsonl

authorship-verification-submission/Dockerfile

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+FROM fschlatt/natural-language-processing-exercises:0.0.1
+
+ADD authorship_verification_submission.py /code/authorship_verification_submission.py
+
+ENTRYPOINT [ "python3", "/code/authorship_verification_submission.py" ]

authorship-verification-submission/authorship_verification_submission.py

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+from pathlib import Path
+
+from tira.rest_api_client import Client
+from tira.third_party_integrations import get_output_directory
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+from sklearn.pipeline import Pipeline
+
+
+
+
+def evaluate_model(model, data, labels):
+    predictions = model.predict(data)
+    accuracy = accuracy_score(labels, predictions)
+    return accuracy
+
+
+
+if __name__ == "__main__":
+    tira = Client()
+
+    # loading train data
+    text_train = tira.pd.inputs(
+        "nlpbuw-fsu-sose-24", "authorship-verification-train-20240408-training"
+    )
+    targets_train = tira.pd.truths(
+        "nlpbuw-fsu-sose-24", "authorship-verification-train-20240408-training"
+    )
+    # loading validation data (automatically replaced by test data when run on tira)
+    text_validation = tira.pd.inputs(
+        "nlpbuw-fsu-sose-24", "authorship-verification-validation-20240408-training"
+    )
+    targets_validation = tira.pd.truths(
+        "nlpbuw-fsu-sose-24", "authorship-verification-validation-20240408-training"
+    )
+
+    tfidf_vectorizer = TfidfVectorizer(max_features=1000)
+
+    # Model Training
+    model = Pipeline([
+        ('vectorizer', tfidf_vectorizer),
+        ('classifier', LogisticRegression())
+    ])
+
+    model.fit(text_train['text'], targets_train['generated'])
+
+
+    val_accuracy = evaluate_model(model, text_validation['text'], targets_validation['generated'])
+    # print("Validation Accuracy:", val_accuracy)
+
+    # make predictions
+    predictions = model.predict(text_validation["text"])
+    text_validation["generated"] = predictions
+    df = text_validation[["id", "generated"]]
+
+    # Save the predictions
+    output_directory = get_output_directory(str(Path(__file__).parent))
+    df.to_json(
+        Path(output_directory) / "predictions.jsonl", orient="records", lines=True
+    )