Book Image

Transformers for Natural Language Processing

By : Denis Rothman
Book Image

Transformers for Natural Language Processing

By: Denis Rothman

Overview of this book

The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.
Table of Contents (16 chapters)
Other Books You May Enjoy

Predicting customer behavior with sentiment analysis

In this section, we will run a sentiment analysis task on several Hugging Face transformer models to see which ones produce the best results and the ones we simply like the best.

We will begin this by using a Hugging Face DistilBERT model.

Sentiment analysis with DistilBERT

Let's run a sentiment analysis task with DistilBERT and see how we can use the result to predict customer behavior.

Open SentimentAnalysis.ipynb and the transformer installation and import cells:

!pip install -q transformers
from transformers import pipeline

We will now create a function named classify, which will run the model with the sequences we send to it:

def classify(sequence,M):
   #DistilBertForSequenceClassification(default model)
    nlp_cls = pipeline('sentiment-analysis')
    if M==1:
    return nlp_cls(sequence)

Note that if you send M=1 to the function, it will display...