Update README.md

README.md

@@ -33,3 +33,48 @@ This model classifies news headlines as either NBC or Fox News.
 
 - Accuracy Score
 
+### Model Evaluation
+```python
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics import classification_report
+
+# Mount to drive
+from google.colab import drive
+drive.mount('/content/drive')
+
+# Load test set
+test_df = pd.read_csv("/content/drive/MyDrive/test_data_random_subset.csv", encoding="Windows-1252")
+
+# Log in w/ huggingface token
+# token: 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
+!huggingface-cli login
+
+# Download the model
+model = hf_hub_download(repo_id = "CIS5190FinalProj/RandomForest", filename = "best_rf_model.pkl")
+
+# Download the vectorizer
+tfidf_vectorizer = hf_hub_download(repo_id = "CIS5190FinalProj/RandomForest", filename = "tfidf_vectorizer.pkl")
+
+# Load the model
+pipeline = joblib.load(model)
+
+# Load the vectorizer
+tfidf_vectorizer = joblib.load(tfidf_vectorizer)
+
+# Extract the headlines from the test set
+X_test = test_df['title']
+
+# Apply transformation to the headlines into numerical features
+X_test_transformed = tfidf_vectorizer.transform(X_test)
+
+# Make predictions using the pipeline
+y_pred = pipeline.predict(X_test_transformed)
+
+# Extract 'labels' as target
+y_test = test_df['label']
+
+# Print classification report
+print(classification_report(y_test, y_pred))