Few-shot prompting with Llama
You're using a Llama model to identify the sentiment of customer reviews from Google and Yelp as Positive
or Negative
. To ensure these labels are consistent for each review, you'll design a few-shot prompt containing three examples.
Here are the examples you want to provide to the model:
- I ordered from this place last night, and I'm impressed! → Positive
- My order was delayed by over an hour without any updates. Disappointing! → Negative
- The food quality is top-notch. Highly recommend! → Positive
Cet exercice fait partie du cours
Working with Llama 3
Instructions
- Complete the few-shot prompt by assigning
Positive
orNegative
to the reviews provided. - Send the prompt to the model with the
"Review"
stop word so the model only responds to one review.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Complete the few-shot prompt
prompt="""Review 1: I ordered from this place last night, and I'm impressed!
Sentiment 1: ____,
Review 2: My order was delayed by over an hour without any updates. Disappointing!
Sentiment 2: ____,
Review 3: The food quality is top-notch. Highly recommend!
Sentiment 3: ____,
Review 4: Delicious food, and excellent customer service!
Sentiment 4:"""
# Send the prompt to the model with a stop word
output = llm(prompt, max_tokens=2, stop=["____"])
print(output['choices'][0]['text'])