RNN for text classification in NLP - Model Pipeline Trace

Model Pipeline - RNN for text classification

This pipeline uses a Recurrent Neural Network (RNN) to read sentences word by word and decide what category the sentence belongs to, like sorting emails into spam or not spam.

Data Flow - 6 Stages

1Raw Text Input

1000 sentences→Collect sentences for classification→1000 sentences

"I love this movie", "This product is terrible"

↓

2Text Tokenization

1000 sentences→Split sentences into words and convert to numbers→1000 sequences x 10 words (max length)

[[12, 45, 78, 0, 0, 0, 0, 0, 0, 0], [34, 56, 89, 23, 0, 0, 0, 0, 0, 0]]

↓

3Embedding Layer

1000 sequences x 10 words→Convert word numbers into word vectors→1000 sequences x 10 words x 50 features

[[[0.1, -0.2, ..., 0.05], ..., [0, 0, ..., 0]], ...]

↓

4RNN Layer

1000 sequences x 10 words x 50 features→Process word vectors in order to capture context→1000 sequences x 64 features

[[0.3, -0.1, ..., 0.2], [0.5, 0.0, ..., -0.3], ...]

↓

5Dense Output Layer

1000 sequences x 64 features→Convert RNN output to class scores→1000 sequences x 2 classes

[[2.1, -1.3], [-0.5, 3.0], ...]

↓

6Softmax Activation

1000 sequences x 2 classes→Convert scores to probabilities→1000 sequences x 2 classes

[[0.89, 0.11], [0.12, 0.88], ...]

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning, accuracy is low but improving
2	0.48	0.75	Loss decreases, accuracy improves significantly
3	0.38	0.82	Model is learning important patterns
4	0.32	0.86	Loss continues to decrease, accuracy rises
5	0.28	0.89	Model converges with good accuracy

Prediction Trace - 5 Layers

Layer 1: Input Sentence

Layer 2: Embedding Layer

Layer 3: RNN Layer

Layer 4: Dense Output Layer

Layer 5: Softmax Activation

Model Quiz - 3 Questions

What does the embedding layer do in this RNN pipeline?

A Turns words into vectors representing their meaning

B Splits sentences into words

C Converts scores to probabilities

D Summarizes the sentence into one number

Key Insight

This visualization shows how an RNN reads sentences word by word, turning them into vectors, then summarizing the meaning to classify text. Training improves the model by lowering loss and increasing accuracy, and the final softmax layer gives clear probabilities for each class.