RNN-based text generation in NLP - Model Pipeline Trace

Model Pipeline - RNN-based text generation

This pipeline uses a Recurrent Neural Network (RNN) to learn patterns in text and generate new sentences one character at a time. It reads sequences of characters, learns how they follow each other, and predicts the next character to create new text.

Data Flow - 5 Stages

1Raw Text Input

1 text file with 10000 characters→Load raw text data→1 text file with 10000 characters

"hello world this is a sample text for training"

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2Text to Sequences

10000 characters→Convert text into overlapping sequences of 40 characters each→9961 sequences x 40 characters

"hello world this is a sample text for train"

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3Character Encoding

9961 sequences x 40 characters→Convert characters to one-hot encoded vectors→9961 sequences x 40 timesteps x 30 unique chars

[[0,0,1,...0], [0,1,0,...0], ...]

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4Train/Test Split

9961 sequences x 40 timesteps x 30 features→Split data into 80% training and 20% testing→7968 training sequences, 1993 testing sequences

Training set: 7968 sequences, Testing set: 1993 sequences

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5Model Training

7968 sequences x 40 timesteps x 30 features→Train RNN model to predict next character→Trained RNN model

RNN with 128 units, output layer with 30 units (softmax)

Training Trace - Epoch by Epoch

Epoch 1: 2.30 #######
Epoch 2: 2.10 ######
Epoch 3: 1.95 #####
Epoch 4: 1.80 ####
Epoch 5: 1.70 ###

Epoch	Loss ↓	Accuracy ↑	Observation
1	2.30	0.25	Model starts learning basic character patterns
2	2.10	0.32	Loss decreases, accuracy improves as model learns
3	1.95	0.38	Model captures more complex sequences
4	1.80	0.44	Better prediction of next characters
5	1.70	0.48	Training converges, model generates more coherent text

Prediction Trace - 4 Layers

Layer 1: Input Sequence Encoding

Layer 2: RNN Layer

Layer 3: Dense Output Layer with Softmax

Layer 4: Character Sampling

Model Quiz - 3 Questions

What does the RNN model learn during training?

A How to sort numbers in a list

B How to classify images into categories

C Patterns of characters and their order in text

D How to translate text into another language

Key Insight

RNNs learn to generate text by remembering sequences of characters and predicting what comes next. As training progresses, the model improves its predictions, shown by decreasing loss and increasing accuracy, enabling it to create coherent new text.