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Machine Learning Techniques for Text

You're reading from   Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introducing Machine Learning for Text 2. Chapter 2: Detecting Spam Emails FREE CHAPTER 3. Chapter 3: Classifying Topics of Newsgroup Posts 4. Chapter 4: Extracting Sentiments from Product Reviews 5. Chapter 5: Recommending Music Titles 6. Chapter 6: Teaching Machines to Translate 7. Chapter 7: Summarizing Wikipedia Articles 8. Chapter 8: Detecting Hateful and Offensive Language 9. Chapter 9: Generating Text in Chatbots 10. Chapter 10: Clustering Speech-to-Text Transcriptions 11. Index 12. Other Books You May Enjoy

Creating a generative chatbot

After our previous short journey on language modeling, let’s focus on the second type of conversational agent and implement a generative chatbot. To make the interaction more enjoyable, we will use a pre-trained model that has been specifically designed for this task. Additionally, we will wrap the implementation around two graphical user interfaces (GUIs) that facilitate the interaction with the model. Finally, we will discuss the steps for tuning the pre-trained model on a different dataset.

Using a pre-trained model

The lack of sufficiently large datasets, processing power, and time are often decisive factors in resorting to a pre-trained model. More importantly, tweaking language models is far from a modest task and requires much expertise. Thus, to create the chatbot, we will utilize DialoGPT (https://huggingface.co/docs/transformers/model_doc/dialogpt), a tunable neural conversational response generation model for multiturn conversations...

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