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---
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datasets:
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- id4thomas/emotion-prediction-comet-atomic-2020
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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---
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# emotion-predictor-Qwen2.5-3B-Instruct
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LLM trained to predict a character's emotional response in the given situation
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* Trained to predict in a structured output format.
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Prediction Performance:
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| Setting | Performance by Emotion|
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| --- | --- |
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| Pretrained | <img src="./assets/qwen2_5-3b-baseline_perf.png" alt="baseline_perf" width="100%" /> |
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| Tuned | <img src="./assets/finetuned_perf.png" alt="trained_perf" width="100%" /> |
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## Quickstart
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The model is trained to predict in the following schema
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```
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from enum import Enum
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from pydantic import BaseModel
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class EmotionLevel(str, Enum):
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na = "na"
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low = "low"
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medium = "medium"
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high = "high"
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class EmotionLabel(BaseModel):
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joy: EmotionLevel
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trust: EmotionLevel
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fear: EmotionLevel
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surprise: EmotionLevel
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sadness: EmotionLevel
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disgust: EmotionLevel
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anger: EmotionLevel
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anticipation: EmotionLevel
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class EntryResult(BaseModel):
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emotion: EmotionLabel
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reason: str
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```
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Using `outlines` package to generate structured predictions
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* system prompt & user template is provided [here](./assets/inference_prompt.yaml)
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```
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import outlines
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from outlines import models
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained("id4thomas/emotion-predictor-Qwen2.5-3B-Instruct")
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tokenizer = AutoTokenizer.from_pretrained("id4thomas/emotion-predictor-Qwen2.5-3B-Instruct")
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# Initalize outlines generator
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outlines_model = models.Transformers(model, tokenizer)
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generator = outlines.generate.json(outlines_model, EntryResult)
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# Generate
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messages = [
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{"role": "system", "content": system_message},
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{"role": "user", "content": user_message}
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]
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input_text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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prediction = generator(input_text)
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>>> EntryResult(emotion=EmotionLabel(joy=<EmotionLevel.na: 'na'>, ...)
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```
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Using endpoint loaded with vllm & OpenAI client package
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* example of using vllm container is provided [here](./assets/run_vllm.sh)
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```
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client = OpenAI(...)
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json_schema = EntryResult.model_json_schema()
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completion = client.chat.completions.create(
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model="id4thomas/emotion-predictor-Qwen2.5-3B-Instruct",
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messages=messages,
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extra_body={"guided_json": json_schema},
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)
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print(completion.choices[0].message.content)
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``` |
