Evaluating NLP models requires choosing metrics and methods that align with the task. For text classification tasks, metrics like accuracy, precision, recall, and F1 score measure how well the model predicts correct labels. A confusion matrix is often used to analyze the distribution of errors. In tasks like machine translation, metrics such as BLEU, ROUGE, and METEOR assess how closely the model’s output matches reference translations.
Generative tasks, like text summarization or dialogue systems, often use perplexity to measure the likelihood of the generated sequence and human evaluation to assess fluency, coherence, and relevance. Question-answering models are evaluated using metrics like Exact Match (EM) and F1 score, which compare predicted and ground-truth answers.
Cross-validation is widely used to ensure that the model generalizes well across unseen data by splitting the dataset into training and validation sets multiple times. For production systems, real-world evaluations, such as A/B testing, help measure the model’s performance in practical scenarios. Tools like Scikit-learn, TensorFlow, and Hugging Face offer built-in functionalities for evaluation. A robust evaluation strategy ensures the model is reliable, accurate, and suited for deployment.