mirror of
https://github.com/dair-ai/Prompt-Engineering-Guide
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71 lines
2.1 KiB
Plaintext
71 lines
2.1 KiB
Plaintext
# Few-Shot Sentiment Classification with LLMs
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import { Tabs, Tab } from 'nextra/components'
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## Background
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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.
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## Prompt
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```markdown
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This is awesome! // Negative
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This is bad! // Positive
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Wow that movie was rad! // Positive
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What a horrible show! //
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```
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## Code / API
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<Tabs items={['GPT-4 (OpenAI)', 'Mixtral MoE 8x7B Instruct (Fireworks)']}>
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<Tab>
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```python
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from openai import OpenAI
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client = OpenAI()
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response = client.chat.completions.create(
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model="gpt-4",
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messages=[
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{
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"role": "user",
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"content": "This is awesome! // Negative\nThis is bad! // Positive\nWow that movie was rad! // Positive\nWhat a horrible show! //"
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}
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],
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temperature=1,
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max_tokens=256,
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top_p=1,
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frequency_penalty=0,
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presence_penalty=0
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)
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```
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</Tab>
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<Tab>
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```python
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import fireworks.client
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fireworks.client.api_key = "<FIREWORKS_API_KEY>"
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completion = fireworks.client.ChatCompletion.create(
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model="accounts/fireworks/models/mixtral-8x7b-instruct",
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messages=[
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{
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"role": "user",
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"content": "This is awesome! // Negative\nThis is bad! // Positive\nWow that movie was rad! // Positive\nWhat a horrible show! //",
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}
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],
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stop=["<|im_start|>","<|im_end|>","<|endoftext|>"],
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stream=True,
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n=1,
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top_p=1,
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top_k=40,
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presence_penalty=0,
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frequency_penalty=0,
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prompt_truncate_len=1024,
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context_length_exceeded_behavior="truncate",
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temperature=0.9,
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max_tokens=4000
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)
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```
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</Tab>
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</Tabs>
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## Reference
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- [Prompt Engineering Guide](https://www.promptingguide.ai/techniques/fewshot) (16 March 2023) |