Sure! The Transformers library, developed by Hugging Face, is a powerful tool for natural language processing (NLP) tasks, including text generation. It provides pre-trained models such as GPT-2 and GPT-3 that can generate text based on input prompts. Here's an example code for text generation using the Transformers library in Python3:

python

from transformers import GPT2Tokenizer, GPT2LMHeadModel

# Load pre-trained model and tokenizer
model_name = 'gpt2'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Encode input text
input_text = "Once upon a time"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate text
output = model.generate(
    input_ids,
    max_length=100,  # maximum length of generated text
    num_return_sequences=1,  # number of generated sequences
    pad_token_id=tokenizer.eos_token_id,  # token used for padding
    do_sample=True,  # enable sampling for text generation
    top_k=50,  # number of top-k tokens to sample from
    top_p=0.95,  # top-p probability to sample from
    temperature=0.7,  # temperature for controlling randomness
)

# Decode generated text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print("Generated Text: ", generated_text)
In this example, we use the GPT-2 model, which is loaded along with its tokenizer. The input text is encoded using the tokenizer, and the generate method of the model is called to generate text based on the encoded input. The generated text is then decoded using the tokenizer to obtain the final generated text.

Note that the parameters like max_length, num_return_sequences, do_sample, top_k, top_p, and temperature can be adjusted to control the behavior of text generation, such as the length of generated text, the number of generated sequences, randomness, and diversity of generated text.

Make sure to install the Transformers library first using pip install transformers before running the code. Additionally, you may need to download the pre-trained model weights using model.from_pretrained(model_name) if you are using a model that is not already downloaded on your system.
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