This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
Unlocking the Power of Prompt Engineering with Hugging Face Transformers
Prompt engineering is a key aspect of natural language processing (NLP) that involves crafting specific input prompts to elicit desired responses from language models. With the Hugging Face Transformers library, we can easily leverage this technique to generate creative and informative outputs.
Example: Generating a Short Story with T5-Base Model
Let’s take a look at a simple example of prompt engineering using Python and the Hugging Face Transformers library:
from transformers import pipeline
# Initialize the T5-Base model for text-to-text generation
prompt_engineer = pipeline("text2text-generation", model="t5-base")
# Define the prompt for generating a short story
prompt = "Write a short story about a chaotic adventure in a mystical forest."
# Use the model to generate a response to the prompt
response = prompt_engineer(prompt)
# Print the generated response
print(response)
In thi…
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This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez