Building a Stable Fable 5 Traces Workflow in Colab: Parsing Tool Calls, Auditing Data, and Training Baselines
rprint(Panel.fit(“[bold]Baseline 1: Predict output_type from context using pure Python Naive Bayes[/bold]”)) model_artifacts = {} classifier_df = df.dropna(subset=[“output_type”]).copy() classifier_df = classifier_df[ classifier_df[“output_type”].astype(str).str.len() > 0 ].copy() if classifier_df[“output_type”].nunique() >= 2 and len(classifier_df) >= 30: X_text = ( classifier_df[“context”] .fillna(“”) .astype(str) .map(lambda text: text[:12000]) .tolist() ) y = classifier_df[“output_type”].astype(str).tolist() train_indices, test_indices = stratified_train_test_indices(y, test_size=0.2, seed=SEED) X_train = [X_text[i]…
