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Evaluation metrics

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Based on the task, different evaluation metrics are used to evaluate the performance of NLP models. Here are some common evaluation metrics used in NLP with their use cases:

  1. Accuracy: The proportion of correct predictions made by the model. It is used in classification tasks.
  2. Precision: The proportion of true positive predictions out of all positive predictions made by the model. It is used in binary classification tasks.
  3. Recall: The proportion of true positive predictions out of all actual positive instances. It is used in binary classification tasks.
  4. F1 Score: The harmonic mean of precision and recall. It is used in binary classification tasks.
  5. BLEU (Bilingual Evaluation Understudy): A metric used to evaluate the quality of machine-translated text. It compares the machine-generated translation against one or more reference translations.
  6. ROUGE (Recall-Oriented Understudy for Gisting Evaluation): A metric used to evaluate the quality of summaries. It compares the machine-generated summary against one or more reference summaries.
  7. Perplexity: A metric used to evaluate language models. It measures how well a probability model predicts a sample. Lower perplexity indicates better performance.
  8. WER (Word Error Rate): A metric used to evaluate the performance of speech recognition systems. It measures the proportion of errors in the output transcription compared to the reference transcription.
  9. CER (Character Error Rate): Similar to WER, but measures the proportion of errors at the character level.
  10. BLEURT (Bilingual Evaluation Understudy with Representations from Transformers): A metric used to evaluate the quality of machine-translated text. It is based on pre-trained transformer models and provides a more fine-grained evaluation compared to BLEU.
  11. METEOR (Metric for Evaluation of Translation with Explicit Ordering): A metric used to evaluate the quality of machine-translated text. It considers unigram precision, recall, and alignment between the machine-generated translation and reference translation.
  12. BERTScore: A metric used to evaluate the quality of machine-translated text. It is based on contextual embeddings from BERT and provides a more fine-grained evaluation compared to BLEU.
  13. SQuAD (Stanford Question Answering Dataset): A dataset and evaluation metric used for question answering tasks. It measures the overlap between the predicted answer and the ground truth answer.
  14. GLUE (General Language Understanding Evaluation): A benchmark used to evaluate the performance of models on a variety of NLP tasks. It consists of multiple tasks with corresponding evaluation metrics.
  15. SuperGLUE: An extension of GLUE that includes more challenging NLP tasks to evaluate model performance.
  16. CoNLL (Conference on Natural Language Learning): A shared task and evaluation metric used for named entity recognition, coreference resolution, and other sequence labeling tasks.
  17. SemEval (Semantic Evaluation): A series of evaluation tasks and metrics used to evaluate various aspects of semantic analysis in NLP.
  18. Pearson Correlation Coefficient: A metric used to evaluate the correlation between predicted and actual values in regression tasks.
  19. Spearman's Rank Correlation Coefficient: A metric used to evaluate the monotonic relationship between predicted and actual values in regression tasks.
  20. Mean Squared Error (MSE): A metric used to evaluate the average squared difference between predicted and actual values in regression tasks.
  21. Mean Absolute Error (MAE): A metric used to evaluate the average absolute difference between predicted and actual values in regression tasks.
  22. Rouge-N: A variant of ROUGE that considers n-grams for evaluating the quality of summaries.
  23. Jaccard Index: A metric used to evaluate the similarity between two sets. It is often used in text summarization tasks to measure the overlap between the predicted summary and the reference summary.
  24. Edit Distance: A metric used to evaluate the similarity between two sequences by measuring the minimum number of operations (insertions, deletions, substitutions) required to transform one sequence into another.
  25. Edit Distance with Real Penalty (WER): A variant of edit distance that assigns different costs to different operations based on their impact on the overall similarity between sequences.
  26. Edit Distance with Substitution Penalty: A variant of edit distance that assigns a higher cost to substitutions compared to insertions and deletions.
  27. Edit Distance with Transposition Penalty: A variant of edit distance that assigns
  28. Edit Distance with Substitution and Transposition Penalty: A variant of edit distance that considers both substitution and transposition operations with different costs.