Book Image

Transformers for Natural Language Processing - Second Edition

By : Denis Rothman
5 (1)
Book Image

Transformers for Natural Language Processing - Second Edition

5 (1)
By: Denis Rothman

Overview of this book

Transformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.
Table of Contents (25 chapters)
18
Other Books You May Enjoy
19
Index
Appendix I — Terminology of Transformer Models

SRL experiments with the BERT-based model

We will run our SRL experiments using the method described in the Setting up the BERT SRL environment section of this chapter. We will begin with basic samples with various sentence structures. We will then challenge the BERT-based model with some more difficult samples to explore the system’s capacity and limits.

Open SRL.ipynb and run the installation cell:

!pip install allennlp==2.1.0 allennlp-models==2.1.0

Then we import the tagging module and a trained BERT predictor:

from allennlp.predictors.predictor import Predictor
import allennlp_models.tagging
import json
predictor = Predictor.from_path("https://storage.googleapis.com/allennlp-public-models/structured-prediction-srl-bert.2020.12.15.tar.gz")

We also add two functions to display the JSON object SRL BERT returns. The first one displays the verb of the predicate and the description:

def head(prediction):
  # Iterating through the json to display...