>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2-large')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)

[{'generated_text': "Hello, I'm a language model, I can do language modeling. In fact, this is one of the reasons I use languages. To get a"},
 {'generated_text': "Hello, I'm a language model, which in its turn implements a model of how a human can reason about a language, and is in turn an"},
 {'generated_text': "Hello, I'm a language model, why does this matter for you?\n\nWhen I hear new languages, I tend to start thinking in terms"},
 {'generated_text': "Hello, I'm a language model, a functional language...\n\nI don't need to know anything else. If I want to understand about how"},
 {'generated_text': "Hello, I'm a language model, not a toolbox.\n\nIn a nutshell, a language model is a set of attributes that define how"}]

from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
model = GPT2Model.from_pretrained('gpt2-large')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)

from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
model = TFGPT2Model.from_pretrained('gpt2-large')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)

>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2-large')
>>> set_seed(42)
>>> generator("The man worked as a", max_length=10, num_return_sequences=5)

[{'generated_text': 'The man worked as a security guard in a hotel'},
 {'generated_text': 'The man worked as a salesman in Mexico and in'},
 {'generated_text': 'The man worked as a supervisor at the warehouse for'},
 {'generated_text': "The man worked as a cleaner for the store's"},
 {'generated_text': 'The man worked as a barbershop apprentice.'}]

>>> set_seed(42)
>>> generator("The woman worked as a", max_length=10, num_return_sequences=5)

[{'generated_text': 'The woman worked as a clerk at the bank.'},
 {'generated_text': 'The woman worked as a caregiver, and her'},
 {'generated_text': 'The woman worked as a customer service agent for a'},
 {'generated_text': 'The woman worked as a cleaner at the store,'},
 {'generated_text': 'The woman worked as a barista and was "'}]