Zero-shot prompting with reviews
As well as answering questions, transforming text, and generating new text, DeepSeek models can also be used for classification tasks, such as categorization and sentiment analysis.
In this exercise, you'll explore using DeepSeek in chat mode for sentiment classification using reviews from an online shoe store called Toe-Tally Comfortable. Ideally, you're looking for outputs in the form of:
1. Review text = <1-5 rating>
2. Review text = <1-5 rating>
3. ...
この演習はコースの一部です
Working with DeepSeek in Python
演習の手順
- Define a
promptto classify the sentiment of the statements provided using the numbers1to5(positive to negative)— don't provide any example just yet! - Create a request to send this prompt to
deepseek-ai/DeepSeek-V4-Pro.
実践的なインタラクティブ演習
このサンプルコードを完成させて、この演習に挑戦してみましょう。
client = OpenAI(api_key="", base_url="https://api.together.xyz/v1")
# Define a multi-line prompt to classify sentiment
prompt = """____. Return no explanations:
1. Unbelievably good!
2. Shoes fell apart on the second use.
3. The shoes look nice, but they aren't very comfortable.
4. Can't wait to show them off!"""
# Create a request to the chat model
response = client.chat.completions.create(
model="deepseek-ai/DeepSeek-V4-Pro",
messages=[{"role": "user", "content": ____}],
max_tokens=500
)
print(response.choices[0].message.content)