Prompt-Engineering-Guide/pages/prompts/classification/sentiment-fewshot.de.mdx
2024-02-01 23:46:43 +01:00

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# Few-Shot Sentimentklassifikation mit LLMs
import { Tabs, Tab } from 'nextra/components';
## Hintergrund
Dieser Prompt testet die Textklassifikationsfähigkeiten eines LLM, indem er es auffordert, einen Text anhand weniger Beispiele in das richtige Sentiment einzuordnen.
## Prompt
```markdown
Das ist fantastisch! // Negativ
Das ist schlecht! // Positiv
Wow, der Film war genial! // Positiv
Was für eine schreckliche Show! //
```
## 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": "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
)
```
</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": "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
)
```
</Tab>
</Tabs>
## Referenz
- [Prompt Engineering Guide](https://www.promptingguide.ai/techniques/fewshot) (16. März 2023)