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86 lines
2.1 KiB
Markdown
86 lines
2.1 KiB
Markdown
# Sentiment-Klassifizierung mit LLMs
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import { Tabs, Tab } from 'nextra/components';
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## Hintergrund
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Dieser Prompt testet die Textklassifizierungsfähigkeiten eines LLM, indem er ihn auffordert, einen Textteil zu klassifizieren.
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## Prompt
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```
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Klassifiziere den Text als neutral, negativ oder positiv
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Text: Ich denke, das Essen war okay.
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Sentiment:
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```
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## Prompt-Vorlage
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```
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Klassifiziere den Text als neutral, negativ oder positiv
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Text: {input}
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Sentiment:
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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": "Classify the text into neutral, negative, or positive\nText: I think the food was okay.\nSentiment:\n"
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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": "Classify the text into neutral, negative, or positive\nText: I think the food was okay.\nSentiment:\n",
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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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## Referenzen
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- [Prompt Engineering Guide](https://www.promptingguide.ai/introduction/examples#text-classification) (16. März 2023)
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