Model selection for tasks in NLP - Model Pipeline Trace

Model Pipeline - Model selection for tasks

This pipeline helps choose the best model for a specific NLP task by comparing different models' performance on the same data.

Data Flow - 6 Stages

1Data Collection

1000 rows x 1 column (text)→Gather raw text data for the NLP task→1000 rows x 1 column (text)

"I love this movie!", "The food was terrible."

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2Preprocessing

1000 rows x 1 column (text)→Clean text: lowercase, remove punctuation, tokenize→1000 rows x variable length tokens

["i", "love", "this", "movie"]

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3Feature Engineering

1000 rows x variable length tokens→Convert tokens to numeric vectors (e.g., TF-IDF or embeddings)→1000 rows x 300 features

[0.0, 0.1, 0.3, ..., 0.0]

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4Model Training

800 rows x 300 features→Train different models (e.g., Logistic Regression, SVM, Neural Network)→Trained models

Model A trained on 800 samples

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5Model Evaluation

200 rows x 300 features→Test models on unseen data and compute accuracy→Accuracy scores per model

Logistic Regression accuracy: 85%

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6Model Selection

Accuracy scores per model→Choose model with highest accuracy for the task→Selected best model

Neural Network selected with 90% accuracy

Training Trace - Epoch by Epoch

Loss: 0.65 |****
Loss: 0.50 |******
Loss: 0.40 |********
Loss: 0.35 |*********
Loss: 0.30 |**********

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning with moderate accuracy
2	0.50	0.75	Loss decreases and accuracy improves
3	0.40	0.82	Model continues to improve
4	0.35	0.86	Training converging with good accuracy
5	0.30	0.89	Final epoch with best performance

Prediction Trace - 4 Layers

Layer 1: Input Text

Layer 2: Vectorization

Layer 3: Model Prediction

Layer 4: Decision

Model Quiz - 3 Questions

Why do we convert text into numeric vectors before training?

A To make text longer

B To remove stop words

C Because models only understand numbers

D To increase dataset size

Key Insight

Choosing the right model for an NLP task involves preparing data properly, training multiple models, and selecting the one with the best performance on unseen data. Watching loss decrease and accuracy increase during training helps confirm the model is learning well.