pip install transformers
python generate_text.py
from transformers import pipeline

# Load the pre-trained GPT-2 model
generator = pipeline('text-generation', model='gpt2')

# Generate some text based on a given prompt
prompt = "The meaning of life is"
generated_text = generator(prompt, max_length=50, num_return_sequences=1)

# Print the generated text
print(generated_text[0]['generated_text'])
{}
region:us
TAGS
#region-us 

pip install transformers
python generate_text.py
from transformers import pipeline

# Load the pre-trained GPT-2 model
generator = pipeline('text-generation', model='gpt2')

# Generate some text based on a given prompt
prompt = "The meaning of life is"
generated_text = generator(prompt, max_length=50, num_return_sequences=1)

# Print the generated text
print(generated_text[0]['generated_text'])
# Load the pre-trained GPT-2 model
generator = pipeline('text-generation', model='gpt2') # Generate some text based on a given prompt
prompt = "The meaning of life is"
generated_text = generator(prompt, max_length=50, num_return_sequences=1) # Print the generated text
print(generated_text[0]['generated_text'])
TAGS
#region-us 
 # Load the pre-trained GPT-2 model
generator = pipeline('text-generation', model='gpt2') # Generate some text based on a given prompt
prompt = "The meaning of life is"
generated_text = generator(prompt, max_length=50, num_return_sequences=1) # Print the generated text
print(generated_text[0]['generated_text'])
TAGS
#region-us 
# Load the pre-trained GPT-2 model
generator = pipeline('text-generation', model='gpt2')# Generate some text based on a given prompt
prompt = "The meaning of life is"
generated_text = generator(prompt, max_length=50, num_return_sequences=1)# Print the generated text
print(generated_text[0]['generated_text'])