In this lesson, you will infer sentiment and topics from product reviews and news articles.
Inferring : set of tasks where the model takes a text as input and performs some kinds of analysis.
- Ex: Extracting lables, names, understand the sentiment of text, etc
Inferring LLM vs classical ML Model
- classical ML Model :
- complex and slow process
- need of a different model for each task (extraction, sentiment analysis..)
- LLM models : much faster and all you need is a
prompt- only need one API (model) to perform many different tasks
Product review
lamp_review = """
Needed a nice lamp for my bedroom, and this one had \
additional storage and not too high of a price point. \
Got it fast. The string to our lamp broke during the \
transit and the company happily sent over a new one. \
Came within a few days as well. It was easy to put \
together. I had a missing part, so I contacted their \
support and they very quickly got me the missing piece! \
Lumina seems to me to be a great company that cares \
about their customers and products!!
"""Sentiment analysis (positive/negative)
prompt = f"""
What is the sentiment of the following product review,
which is delimited with triple backticks?
Review text: '''{lamp_review}'''
"""
response = get_completion(prompt)
print(response)Completion :
The sentiment of the product review is positive.
Check out the notebook to see more code examples.
Main course :
Introducing the Hugging Face LLM Inference Container for Amazon SageMaker :
llm-inference - Kaggle