How to use GPT model for text generation from entry to mastery of GPT

The GPT model is a natural language processing technology that has shown excellent results in tasks such as text generation. In this post, we will detail how to use the GPT model for text generation in the Tensorflow2.x environment.
 

Preparation

Before using the GPT model for text generation, we need to install and prepare the corresponding tools and technologies:
Tensorflow2.x and Transformers library: Tensorflow2.x is a deep learning framework, and Transformers library is used to build and use natural language processing models Python library. You can install both libraries using pip.
# Install Tensorflow2.x using pip

pip install tensorflow==2.3.1

# Install Transformers library using pip

pip install transformers==3.5.1

GPT models: GPT models are pre-trained models that you can find and download in the Transformers repository.

from transformers import TFGPT2LMHeadModel

# load the GPT model

model = TFGPT2LMHeadModel.from_pretrained("gpt2")

Tokenizer: GPT models need to accept text input, and you need to use the GPT2Tokenizer class to convert the text into tokens usable by the model.

from transformers import GPT2Tokenizer

# Load Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

text generation

With the above preparations, we can use the GPT model for text generation. The following is a simple example of how to use the GPT model to input a text and generate a natural language text:
# define input

input_text = "Once upon a time there was a"
input_ids = tokenizer.encode(input_text, return_tensors="tf")

# generate text

output = model.generate(input_ids, max_length=1000, do_sample=True, temperature=0.7)

# output text

output_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_text)

In this example, we first use a Tokenizer to encode input text into IDs that can be used by the GPT model.