AI Lesson 79 – NLP Evaluation Metrics | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

Lesson 79: NLP Evaluation Metrics

Building an NLP model is only half the work. The other half is measuring how good the model actually is. NLP evaluation metrics help us understand whether a model is performing well or needs improvement.

Without proper evaluation, we cannot trust model outputs, especially in real-world applications like translation, sentiment analysis, or text generation.

Real-World Connection

When companies deploy chatbots, recommendation engines, or translation systems, they continuously evaluate model quality. Poor evaluation can lead to wrong decisions, customer dissatisfaction, or even legal risks.

Evaluation metrics act like performance reports for AI models.

Why Evaluation Is Important in NLP

Natural language is complex and subjective. Two answers can both look reasonable but differ in quality. Evaluation metrics help quantify this quality in a measurable way.

  • Compare different models
  • Track improvements over time
  • Detect model degradation

Common NLP Evaluation Metrics

Different NLP tasks use different metrics. There is no single metric that fits all tasks.

Accuracy

Accuracy measures how many predictions were correct out of the total predictions. It is commonly used in classification tasks. 


accuracy = correct_predictions / total_predictions
print(accuracy)

Precision and Recall

Precision measures how many predicted positives were actually correct. Recall measures how many actual positives were successfully identified.

These metrics are especially important in tasks like spam detection or medical text analysis.

F1 Score

The F1 score is the harmonic mean of precision and recall. It balances both metrics into a single score. 


from sklearn.metrics import f1_score

y_true = [1, 0, 1, 1]
y_pred = [1, 0, 0, 1]

print(f1_score(y_true, y_pred))
0.80

Metrics for Text Generation and Translation

For tasks like machine translation and text generation, exact matching is not reliable. Instead, similarity-based metrics are used.

BLEU Score

BLEU measures how similar generated text is to a reference translation based on overlapping word sequences.

  • Higher BLEU score indicates better translation quality
  • Widely used in machine translation

ROUGE Score

ROUGE measures overlap between generated text and reference text. It is commonly used in summarization tasks.

Human Evaluation

Automatic metrics cannot fully capture meaning, tone, or correctness. Human evaluation is often used alongside automated metrics to judge fluency, relevance, and coherence.

In production systems, both human and automated evaluations are combined.

Challenges in NLP Evaluation

  • Multiple correct answers for the same input
  • Context-dependent quality
  • Bias in evaluation datasets

Choosing the right metric is critical for building reliable NLP systems.

Practice Questions

Practice 1: What do NLP evaluation metrics measure?



Practice 2: Which metric balances precision and recall?



Practice 3: Which metric is commonly used for translation evaluation?



Quick Quiz

Quiz 1: Which metric measures correctness of positive predictions?





Quiz 2: Which metric is used for summarization tasks?





Quiz 3: What complements automatic NLP metrics?





Coming up next: NLP Use Cases — how NLP is applied across industries in real-world systems.

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