Evaluating Text Models

Evaluating text models involves using metrics specifically tailored to assess the performance of text classification tasks. Metrics such as precision, recall, F1 score, and confusion matrices provide detailed insights into how accurately and effectively our models classify textual data, allowing us to interpret and improve model outcomes. However, although it falls outside the scope of this book, LLMs require far more complex techniques to evaluate their performance, especially when generating text rather than using it in more simplistic ML problems like classification.

Getting ready

Let's set up the necessary libraries and dataset for evaluating text classification models.

1. Load the libraries:

   from sklearn.feature_extraction.text import TfidfVectorizer
   from sklearn.model_selection import train_test_split
   from sklearn.linear_model import LogisticRegression
   from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix, classification_report...