Accuracy is the most straightforward metric, calculated as the ratio of correct predictions to the total number of predictions.

Recall, also known as sensitivity, measures the ability of a model to identify true positives.

F1-score is the harmonic mean of precision and recall, providing a balanced assessment of a model's performance.

Perplexity is a measure of how well a language model predicts the next word in a sequence.

BLEU (Bilingual Evaluation Understudy) is a popular metric for evaluating the quality of machine-generated text by comparing it to human-generated references.

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics specifically designed for evaluating the quality of machine-generated summaries.

Precision measures the ability of a model to avoid false positives.

F1-score is often used for named entity recognition tasks due to its balanced consideration of precision and recall.

METEOR (Metric for Evaluation of Translation with Explicit Ordering) is a metric specifically designed for evaluating the quality of machine-generated translations.

F1-score is often used for question answering tasks due to its balanced consideration of precision and recall.

F1-score considers both precision and recall, penalizing models that have high precision but low recall or vice versa.

F1-score is often used for text classification tasks due to its balanced consideration of precision and recall.

BLEU is often used for evaluating the quality of machine-generated dialogue due to its ability to measure fluency and coherence.

F1-score is often used for sentiment analysis tasks due to its balanced consideration of precision and recall.

ROUGE is a set of metrics specifically designed for evaluating the quality of machine-generated text summarization.