HelpingAI/HAI-SER-v0.1

❤️ HAI-SER-v0.1

[📜 License](https://helpingai.co/license) | [🌐 Website](https://helpingai.co)

🌟 About HAI-SER-v0.1

HAI-SER-v0.1 is the first version of HelpingAI's Structured Emotional Reasoning (SER) model designed to enhance the emotional intelligence of conversational AI. It integrates emotional processing with logical reasoning, enabling more empathetic and meaningful human-AI interactions. This model aims to address the limitations of traditional models by incorporating key components for understanding and responding to human emotions.

Research Paper: SER: HelpingAI

💡 Core Concepts of SER

The Structured Emotional Reasoning (SER) framework includes the following key components:

• Emotional Vibe Check: Gauges the overall emotional tone of the conversation.
• Mind-State Analysis: Delves into the user's psychological state.
• Root Cause Exploration: Uncovers the underlying factors influencing emotional states.
• Growth Potential Evaluation: Assesses opportunities for future development and adaptation.

These components work together to allow the AI to respond with improved emotional sensitivity and accuracy, making it suitable for applications like mental health, customer support, and education where emotional understanding is crucial.

💻 Implementation

This model is designed to be integrated into existing conversational AI pipelines. The current version can be utilized for research and experimentation.

Using Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load HAI-SER-v0.1
model = AutoModelForCausalLM.from_pretrained("HelpingAI/HAI-SER-v0.1")
tokenizer = AutoTokenizer.from_pretrained("HelpingAI/HAI-SER-v0.1")

# Example usage
chat = [
    {"role": "system", "content": "You are an emotionally intelligent AI assistant who always thinks step by step before answering a user's."},
    {"role": "user", "content": "I am feeling really frustrated with my work today."}
]

inputs = tokenizer.apply_chat_template(
    chat,
    add_generation_prompt=True,
    return_tensors="pt"
)

outputs = model.generate(
    inputs,
    max_new_tokens=128,
    temperature=0.7,
    top_p=0.9,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

⚙️ Training Details

Training Data

• Trained on a dataset focused on enhancing emotional intelligence in conversations.
• Includes dialogues, emotional cues, and diverse scenarios.

Capabilities

• Identifies and interprets emotional cues in text.
• Provides empathetic and context-aware responses.
• Demonstrates basic ability to perform Emotional Vibe Check, Mind-State Analysis, Root Cause Exploration, and Growth Potential Evaluation.

⚠️ Limitations

• Model is in early stages and may not fully capture nuanced emotions.
• Responses may occasionally lack depth or fail to fully address complex emotional states.
• Performance may vary based on the specific context of the interaction.
• Not fully tested in all real-world scenarios.

Safety

• Designed to avoid generating responses that are insensitive or harmful.
• Does not provide advice in medical or mental health context.

📚 Citation

@misc{haiser2024,
  author = {HelpingAI Team},
  title = {HAI-SER-v0.1: Structured Emotional Reasoning for Empathetic AI},
  year = {2024},
  publisher = {HelpingAI},
  journal = {HuggingFace},
  howpublished = {\url{https://huggingface.co/HelpingAI/HAI-SER-v0.1}}
}

Built with dedication, precision, and passion by HelpingAI

Website • GitHub • Discord • HuggingFace