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Unleashing the Power and Potential of Text Generation

This article explores generation and its potential impact on language and communication. We'll dive into the technology, how it works, and its applications.
Mostafa Ibrahim
Created on January 26|Last edited on April 13

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Introduction

Text generation, a rapidly advancing field of natural language processing, enables machines to produce human-like text. It has become a vital tool in artificial intelligence, with applications ranging from chatbots to automated writing.
In this article, we delve into the world of text generation to understand the technology behind it, how it works, and its various applications in different industries.
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Here's what we'll discuss:

Table of Contents

IntroductionHow Is Text Generation Defined?What is Tokenization What is the Use of Text Generation?What is Text Generation in NLP?What is the best model for Text Generation?What is Text Generation using RNNs?Tutorial on Text Generation Using KerasConclusion


How Is Text Generation Defined?

Text generation is the task of automatically producing written language that is similar to human-written text. This is typically done using a machine learning model called a language model, which is trained on a large dataset of human-written text.
Once the model is trained, it can generate new text by predicting the next word in a sentence, given the previous words. The model uses this information to generate new text similar to the text it was trained on.
There are different variations of text generation, such as language modeling, summarization, dialogue generation, and text completion. These models can be used in various applications, such as natural language processing, machine translation, text-to-speech synthesis, and more. The quality of the generated text depends on the quality of the dataset used to train the model and the choice of the model architecture.

How Many Layers Are There in Text Generation?

The number of layers in a text generation model can vary depending on the architecture used. Some common architectures used in text generation include Recurrent Neural Networks (RNNs), Transformer Models, Gated Recurrent units (GRU), and Long Short-term Memory (LSTM).
For example, RNNs use a sequence of layers, where each layer processes the input and output of the previous layer, as shown in the image below.
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The architecture of transformer models consists of stacking layers, each composed of multi-head self-attention and a feed-forward neural network, as illustrated in the following image.
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Meanwhile, Gated Recurrent Unit (GRU) and Long Short-term Memory (LSTM) use a sequence of layers, similar to RNNs, but with gating mechanisms to allow the model to learn long-term dependencies more effectively. The number of layers can affect the model's ability to learn long-term dependencies in the text, and it can also impact the complexity of the model and its performance.
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What is Tokenization

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Tokenization is a crucial step in text generation because it helps the model understand the individual words and phrases in the text. By breaking down the text into smaller, more manageable pieces, the model can better learn the patterns and dependencies in the text. For example, the tokenization process will split the string “Natural Language Processing” into 3 individual words, making the model's understanding simpler. More modern tokenizations techniques often break words into subwords or even characters.
Tokenization also helps with preprocessing the text. For example, by removing stop words and normalizing the text, which are all important steps in preparing the data for text generation. Additionally, tokenization allows for a more efficient training process, as the model can learn from individual words rather than the entire text. In simple terms, tokenization makes text generation more accurate, efficient, and effective.

What is the Use of Text Generation?

Text generation can be used to generate new, original content in a variety of sectors as well as automate many processes that would otherwise be completed manually, such as authoring, summarizing, and translating text. These uses include:
Language modeling: Text generation can be used to predict the next word in a sentence, which can be helpful for things like speech recognition and text-to-speech synthesis.
Dialogue generation: Text generation can generate text appropriate for a specific context, like a conversation between two people. This is often used in things like chatbots and virtual agents.
Auto text completion: Text generation can be used to complete a given text, making it grammatically correct and semantically meaningful. This can be used in areas like writing assistance and content creation.
Creative writing: Text generation can create new stories, poetry, and other forms of creative writing.
Text summarization: Text generation can automatically create a summary of a longer piece of text. This can be useful in areas like news summarization and document summarization.

What Is an Example of a Real-Life Text Generation Model?

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ChatGPT and GPT4 are both tremendous demonstrations of the true potential of text generation technology. ChatGPT is a state-of-the-art language model developed by OpenAI. It is trained on a massive amount of text data and can generate coherent, human-like text, which is often indistinguishable from human writing.
This is due to its advanced natural language processing capabilities, which allow it to understand and generate text in a way that closely mimics human language patterns. It is considered one of the most advanced text generation models and is widely used in various applications such as chatbots, text summarization, text completion, and so on.
An example of ChatGPT answering a simple question

What is Text Generation in NLP?

NLP, or Natural Language Processing, is an exciting field of study that explores the intricacies of human language and how to use computers to process it. It is a multi-disciplinary field combining computer science, linguistics, and artificial intelligence elements to create powerful algorithms and models that can understand, interpret, and generate human language.
NLP is the technology that powers chatbots, virtual assistants, and machine translation, to name just a few examples. It allows us to communicate with machines in natural language, making technology more accessible and user-friendly.

What is the best model for Text Generation?

The best model for text generation depends on the specific task and dataset being used, the specific task, the desired output, and the available resources. It's always recommended to try different models and compare their performance on the specific task and dataset.
This said, some of the most popular models are:
Recurrent Neural Networks (RNNs) such as LSTMs and GRUs are popular for text generation because they can learn long-term dependencies in the text. For example, an LSTM-based model can be used to generate new text that is similar to a given input text.
The seq2Seq model is a neural network architecture that is particularly well-suited to sequence-to-sequence problems. For example, a Seq2Seq model can be used to generate the response of a chatbot.
Transformer models such as GPT and BERT are also popular for text generation because they use multi-head self-attention and are pre-trained on large datasets to generate high-quality text. For example, GPT-2 can be fine-tuned to generate news articles, stories, or even poetry.
Variational Autoencoder (VAE) is a deep generative model that can generate new examples by sampling from latent space. For example, a VAE-based model can generate new product descriptions.

What is Text Generation using RNNs?

Recurrent Neural Networks (RNNs) have played a significant role in advancing the text generation field. Essentially, RNNs are a type of neural network that is great at learning patterns and dependencies in text, making them ideal for generating new text similar to human-written text.
Here's a breakdown of the process of text generation using RNNs:
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  1. Data collection: collecting a dataset of human-written text.
  2. Data processing and cleaning: this step includes cleaning, normalizing, and tokenizing the text.
  3. Model training: training an RNN model on the dataset. This step involves defining the model's architecture, including the number of layers, neurons, and activation functions.
  4. Text generation and testing: using the trained model to generate new text by providing it with a seed text or a random starting point. This newly generated text will be used to test the model's overall performance
  5. Model improving: improving the model's performance, resulting in more human-like text generation.
The advantages of text generation using RNNs include:
  • The ability to learn long-term dependencies in sequential data is crucial for text generation.
  • Handling variable-length input and output sequences, making them suitable for text generation tasks.
  • Can be trained on relatively small datasets.
  • Generating text that is more natural and coherent. This is because RNNs can learn long-term dependencies in the text and generate text that is similar to the training data.
  • Being computationally less expensive than other models, such as GPT, BERT, VAE, GANs, etc.
Remember that RNNs are not the only option for text generation, other models, which we have stated earlier, such as GPT, BERT, VAE, and GANs, also have their own strengths and use cases. But one thing is for sure, text generation using RNNs is a great way to generate new text that is similar to human-written text. And with the continuous advancements in the field, it's exciting to see what the future holds for text generation.

Tutorial on Text Generation Using Keras

Step 1: Import the necessary libraries

To start with, we import the pad_sequences function from Keras; this function is used to perform pad sequencing on varying lengths to reach the same length so that they can be input into a neural network.
Moving on, we will import multiple layers from the Keras library, such as the embedding, LSTM, dense, and dropout layers. We'll utilize these in our text generation model.
The embedding layer is used to map words to a dense vector representation, LSTM is used to create a long short-term memory layer. Dense is used to create a fully connected layer. Dropout is used for regularization to prevent overfitting.
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Embedding, LSTM, Dense, Dropout
from keras.preprocessing.text import Tokenizer
from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.utils import np_utils
import numpy as np

Step 2: Building our model

In this part of the code, we will add the above-specified layers into our text generation model, starting with the embedded layer. In this function, the first argument is the vocabulary size, which is the number of unique words in the input text.
The second argument is the dimensionality of the embedding, which is set to 10. The third argument is the input length, which is the maximum length of the input sequences minus 1 to account for the label.
Moving on to the LSTM layer, we will have the layer contain 150 units and return the full sequence of outputs for each input sequence.
The dropout layer is used to prevent overfitting. It will drop 20% of the neurons during the training process.
The dense layer outputs a probability distribution over the vocabulary for each word in the input sequence.
def create_model(vocab_size, max_length):
	model = Sequential()
	model.add(Embedding(vocab_size, 10, input_length=max_length-1))
	model.add(LSTM(150, return_sequences = True))
	model.add(Dropout(0.2))
	model.add(LSTM(100))
	model.add(Dense(vocab_size/2, activation='relu'))
	model.add(Dense(vocab_size, activation='softmax'))
	model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
	model.summary()
	return model

Step 3: Create our dataset

This text is going to be used to train the text generation model. For the sake of simplicity, we will utilize a small dataset for the example, to get better results, you will need a large dataset that consists of many text examples.
data = """ Jack and Jill went up the hill To fetch a pail of water Jack fell down and broke his crown And Jill came tumbling after"""

Step 4: Preparing the dataset

Here's some simple code to prep our dataset: 
#Identifty the tokenizer
tokenizer = Tokenizer()

def dataset_preparation(data):
	# basic cleanup
	corpus = data.lower().split("\n")

Step 4a: Tokenization of the data set

As stated earlier, tokenization is the process of breaking down a string of text into smaller units called tokens. Tokens can be words, phrases, or any other meaningful text units.
tokenizer.fit_on_texts(corpus)
total_words = len(tokenizer.word_index) + 1

Step 4b: create an input sequence using a list of tokens

The input sequences are a list of lists, where each inner list represents a sequence of tokens (words or phrases) that will be used as input for the model.
input_sequences = []
for line in corpus:
	token_list = tokenizer.texts_to_sequences([line])[0]
	for i in range(1, len(token_list)):
		n_gram_sequence = token_list[:i+1]
		input_sequences.append(n_gram_sequence)

Step 4c: Perform pad sequencing

Padding is the process of adding extra elements, such as 0, to a sequence of elements so that all sequences in a dataset have the same length. 
	max_sequence_len = max([len(x) for x in input_sequences])
	input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))

Step 4d: Create predictors and label

The below lines of code are used to prepare the input sequences and labels for the text generation model.
	predictors, label = input_sequences[:,:-1],input_sequences[:,-1]
	label = np_utils.to_categorical(label, num_classes=total_words)

	return predictors, label, max_sequence_len, total_words
The below code runs the actual dataset_preparation() function that performs all the above step 4 tasks.
predictors, label, max_sequence_len, total_words = dataset_preparation(data)

Step 5: Create the final model

model = create_model(total_words, max_sequence_len)

Step 6: Fit the model

model.fit(predictors, label, epochs=100, batch_size=64)

Step 7: Generate some text

seed_text = "Jack and Jill went"
next_words = 100

for _ in range(next_words):
	token_list = tokenizer.texts_to_sequences([seed_text])[0]
	token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
	predicted = model.predict(token_list,  verbose=0)
	output_word = ""
	for word,index in tokenizer.word_index.items():
		if index == np.argmax(predicted):
			output_word = word
			break
	seed_text += " "+output_word
print(seed_text)

Conclusion

Running the code above may not result in great outputs because our dataset here is rather small. But this is a great general rubric to follow for creating text generation models. We've a good deal of pieces on LLMs you can read to see the

Tags: Articles, Beginner, Text Generation, GenAI, NLP

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