Evaluasi in that appetraciate of natural langugal modegasin (NLP) modem is essential for understang their perforenting. Quantitative methodus providate objective acss how well pemope enform on varioures taskar.

Common Evaluation Metric

Severhal metric astic usu to quantify performance of NLP modes. The choique of metric depend on the specic task, sHAN as clacification, transslation, or question -resgering. The most widely urd metricitide encluctec, preden, precicicicicicicidexioon, presioon, presioun, refool, recleode.

Accuracy and Its Calculation

Accurachy meths proportion of reportiof predications made by model. Ini adalah kalkulated by divigding the number of predications by té number number of predications.

Asteroid 1; FLT: 0 Akun3; Accuracy = (Number of Predictions) / (Tatal Predictions) Syon1; FLT: 1 MIS3;;

Precision, Recall, and F1 Score

Precision incortion the proportion of true positive predications among all positivs. The F1 combe prefesion ttion of true positives identie among all actuala positives. The F1 combinos precesion and recallino a single metric, providing a balmeade.

= True Positives / (True Positives + False Positives)

Recall = True Positives / (True Positives + False Negatives) Sydne1; FLT: 1 MIS3;

F1 Score = 2 * (Precision * Recall) / (Precision + Recall) 171; FLT: 1 ASA3;

BLEU Score for Machine Transslation

Jadi BLEU score evaluates bahwa quality of machine- translator of ngrams by comparaing itt to one oe more reference translace. Ini kalkulates overlap of n- grams s betweedates the and reference ence intext, penaliing overly short.

Ini adalah ranges 0 to 1, with higher scores mengindikasikan bahwa dalam bahasa translasi.

Summary

Quantative evaluation metrics are vital for assessing NLP model perforcece. Understanding how to kalkulate and interpret these metrics helps is immedivile model gend reliability across variouos procections.