Prompt-Engineering-Guide/pages/prompts/classification/sentiment.en.mdx
Elvis Saravia 1238452236 prompt hub
2024-01-20 12:12:16 -06:00

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# Sentiment Classification with LLMs
import { Tabs, Tab } from 'nextra/components'
## 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
<Tabs items={['GPT-4 (OpenAI)', 'Mixtral MoE 8x7B Instruct (Fireworks)']}>
<Tab>
```python
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
)
```
</Tab>
<Tab>
```python
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
)
```
</Tab>
</Tabs>
## Reference
- [Prompt Engineering Guide](https://www.promptingguide.ai/introduction/examples#text-classification) (16 March 2023)