model.predict

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1) # Train the model model = RandomForestClassifier(n_estimators=100, random_state=1) model.fit(X_train, y_train) ``` #### Step 2: Pre-Fetching Logic I…

3. **Strategy 3**: Uses pre-trained embeddings. For demonstration purposes, we use a random matrix, but in practice, you would use a pre-trained embedding matrix. 4. **Strategy 4**: Adds positional information to the embeddings. This is don…

3. **Extract Context Window**: Define a lambda layer to extract the context window around each token. The context window is defined by the `context_size`, which determines the number of surrounding tokens to consider. 4. **Flatten Context W…

3. **Extract Context Window**: Define a lambda layer to extract the context window around each token. The context size is calculated dynamically based on the query length. 4. **Flatten Context Window**: Flatten the context window tensor to …

predictions = model.predict(X_test_tfidf) # Calculate the recall score recall = recall_score(y_test, predictions) print(f'Recall score: {recall:.3f}') # Print classification report and confusion matrix print(classification_report(y_test, …

X_train, X_val = X[train_index], X[val_index] y_train, y_val = y[train_index], y[val_index] # Fit the model on the training data model.fit(X_train, y_train) # Predict on the validati…