Text preprocessing for RNNs in PyTorch - Model Pipeline Trace

Model Pipeline - Text preprocessing for RNNs

This pipeline shows how raw text data is cleaned and prepared step-by-step to be used as input for a Recurrent Neural Network (RNN). It converts sentences into numbers that the RNN can understand.

Data Flow - 7 Stages

1Raw Text Input

1000 sentences (variable length)→Collect raw sentences from dataset→1000 sentences (variable length)

"I love cats", "Deep learning is fun"

↓

2Lowercasing and Cleaning

1000 sentences (variable length)→Convert all letters to lowercase and remove punctuation→1000 sentences (variable length)

"i love cats", "deep learning is fun"

↓

3Tokenization

1000 sentences (variable length)→Split sentences into words (tokens)→1000 lists of tokens (variable length)

["i", "love", "cats"], ["deep", "learning", "is", "fun"]

↓

4Vocabulary Building

All tokens from 1000 sentences→Create a dictionary mapping each unique word to an integer index→Vocabulary size = 5000 words

{"i":1, "love":2, "cats":3, "deep":4, "learning":5, "is":6, "fun":7}

↓

5Numerical Encoding

1000 lists of tokens→Replace each token with its integer index from vocabulary→1000 lists of integers (variable length)

[1, 2, 3], [4, 5, 6, 7]

↓

6Padding

1000 lists of integers (variable length)→Add zeros to make all sequences the same length (max length = 6)→1000 lists of integers (length = 6)

[1, 2, 3, 0, 0, 0], [4, 5, 6, 7, 0, 0]

↓

7Tensor Conversion

1000 lists of integers (length = 6)→Convert lists into PyTorch tensors for model input→Tensor of shape (1000, 6)

tensor([[1, 2, 3, 0, 0, 0], [4, 5, 6, 7, 0, 0], ...])

Training Trace - Epoch by Epoch

Loss
1.2 |****
0.9 |***
0.7 |**
0.55|*
0.45| 
    +------------
     Epochs 1-5

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning; loss is high and accuracy is low.
2	0.9	0.60	Loss decreases and accuracy improves as model learns patterns.
3	0.7	0.72	Continued improvement; model is fitting training data better.
4	0.55	0.80	Loss drops further; accuracy reaches a good level.
5	0.45	0.85	Model converges with low loss and high accuracy.

Prediction Trace - 4 Layers

Layer 1: Input Embedding Layer

Layer 2: RNN Layer

Layer 3: Fully Connected Layer

Layer 4: Softmax Activation

Model Quiz - 3 Questions

Why do we pad sequences to the same length before feeding them to the RNN?

A To increase the vocabulary size

B To remove stop words from sentences

C Because RNNs require inputs of equal length for batch processing

D To convert words into numbers

Key Insight

Text preprocessing transforms raw sentences into fixed-size numeric tensors that RNNs can process. Proper cleaning, tokenization, and padding are essential for effective learning and stable training.