Text Generation

Text generation involves creating coherent text based on a prompt. This is useful for applications like chatbots, content creation, and creative writing assistance.

Hands-on Example: Text Completion

from transformers import pipeline

# Initialize the text generation pipeline
generator = pipeline("text-generation", model="gpt2")

# Generate text from prompts
prompts = [
    "In the distant future, artificial intelligence",
    "The best way to learn programming is",
    "Climate change has affected many regions, leading to"
]

# Generate and display completions
for prompt in prompts:
    completions = generator(prompt, max_length=50, num_return_sequences=2)
    print(f"Prompt: {prompt}")
    
    for i, completion in enumerate(completions):
        print(f"Completion {i+1}: {completion['generated_text']}")
    
    print("-" * 50)
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The text generation pipeline continues text from the given prompts, producing creative and contextually relevant completions. The default model is GPT-2, but you can specify other models as needed.

Try It Yourself:

1. Experiment with different prompts related to your interests.
2. Try adjusting parameters like max_length, num_return_sequences, and temperature (controls randomness).
3. Use different models like EleutherAI/gpt-neo-1.3B for potentially better completions.