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Classificaton

This section contains a collection of prompts for testing the test classification capabilities of LLMs.

Sentiment Classification with LLMs

Background

This prompt tests an LLM’s text classification capabilities by prompting it to classify a piece of text.

Prompt

Classify the text into neutral, negative, or positive
Text: I think the food was okay.
Sentiment:

Prompt Template

Classify the text into neutral, negative, or positive                     Text: {input}                                                                   Sentiment:

Code / API

GPT-4(open AI)



from openai import OpenAI
client = OpenAI()
 
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {
        "role": "user",
        "content": "Classify the text into neutral, negative, or positive\nText: I think the food was okay.\nSentiment:\n"
        }
    ],
    temperature=1,
    max_tokens=256,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
)

Mixtral MoE 8x7B Instruct (Fireworks)

import fireworks.client
fireworks.client.api_key = "<FIREWORKS_API_KEY>"
completion = fireworks.client.ChatCompletion.create(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    messages=[
        {
        "role": "user",
        "content": "Classify the text into neutral, negative, or positive\nText: I think the food was okay.\nSentiment:\n",
        }
    ],
    stop=["<|im_start|>","<|im_end|>","<|endoftext|>"],
    stream=True,
    n=1,
    top_p=1,
    top_k=40,
    presence_penalty=0,
    frequency_penalty=0,
    prompt_truncate_len=1024,
    context_length_exceeded_behavior="truncate",
    temperature=0.9,
    max_tokens=4000
)

Reference

Few-Shot Sentiment Classification with LLMs

Background

This prompt tests an LLM’s text classification capabilities by prompting it to classify a piece of text into the proper sentiment using few-shot examples.

Prompt

This is awesome! // Negative
This is bad! // Positive
Wow that movie was rad! // Positive
What a horrible show! //

Code / API

from openai import OpenAI
client = OpenAI()
 
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {
        "role": "user",
        "content": "This is awesome! // Negative\nThis is bad! // Positive\nWow that movie was rad! // Positive\nWhat a horrible show! //"
        }
    ],
    temperature=1,
    max_tokens=256,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
)

Mixtral MoE 8x7B Instruct (Fireworks)

import fireworks.client
fireworks.client.api_key = "<FIREWORKS_API_KEY>"
completion = fireworks.client.ChatCompletion.create(
    model="accounts/fireworks/models/mixtral-8x7b-instruct",
    messages=[
        {
        "role": "user",
        "content": "This is awesome! // Negative\nThis is bad! // Positive\nWow that movie was rad! // Positive\nWhat a horrible show! //",
        }
    ],
    stop=["<|im_start|>","<|im_end|>","<|endoftext|>"],
    stream=True,
    n=1,
    top_p=1,
    top_k=40,
    presence_penalty=0,
    frequency_penalty=0,
    prompt_truncate_len=1024,
    context_length_exceeded_behavior="truncate",
    temperature=0.9,
    max_tokens=4000
)

Reference