Model Performance Metrics | Course and Power Point for Bots

The article discusses key benchmarks in natural language processing evaluation, including Perplexity, BLEU Score, ROUGE Score, Word Error Rate, and Training Time, highlighting their significance in assessing model performance and text generation quality.
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Benchmark Significance
Perplexity Measures how well the model predicts a sample of text. Lower perplexity indicates better performance in predicting the next word in a sequence.
BLEU Score Evaluates the quality of machine-generated text by comparing it to human-generated reference texts. Higher BLEU score indicates better quality of generated text.
ROUGE Score Measures the overlap between machine-generated text and reference summaries. Higher ROUGE score signifies better content overlap.
Word Error Rate (WER) Calculates the difference between predicted and reference text in terms of word-level errors. Lower WER indicates higher accuracy in generated text.
Training Time Reflects the time taken to train the model on a specific dataset. Shorter training time is desirable for efficient model development.


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