README.md

---
annotations_creators: [human-generated]
language: [en]
license: mit
multilinguality: monolingual
pretty_name: Empathic Conversations
size_categories: [10K<n<100K]
source_datasets: []
task_categories: [text-generation]
task_ids: [dialogue-modeling]
---

# EmpathicConversations

**EmpathicConversations** is a human-curated dataset designed to train and evaluate conversational AI systems that offer emotionally intelligent, supportive, and non-judgmental responses in therapeutic settings.

## 🧠 Purpose

This dataset serves as a foundation for building AI therapy assistants and mental wellness chatbots. It emphasizes empathy, active listening, and emotional support, aiming to make AI more compassionate and context-aware in sensitive conversations.

## 📁 Dataset Structure

- **Split**: `train`
- **Fields**:
  - `context` (string): The user's emotionally loaded input or concern.
  - `response` (string): A thoughtful, helpful, and empathetic AI response.

### Example

| Context | Response |
|--------|----------|
| I'm going through some things with my feelings and myself. I barely sleep and I do nothing but think... | First thing I'd suggest is getting the sleep you need or it will impact how you think and feel. I'd look at... |

## ⚙️ Use Cases

- Fine-tuning conversational LLMs for therapy assistance.
- Creating emotionally sensitive dialogue agents.
- Researching AI emotional intelligence and ethical chatbot development.

> ⚠️ **Disclaimer**: This dataset is intended for research and educational purposes. It is **not a substitute for professional mental health care**.

## 🪪 License

This dataset is released under the MIT License — free to use, modify, and share.

## 👤 Author

**Mohamed Elaakeb (samdak93)**  
Hugging Face: [@samdak93](https://huggingface.co/samdak93)  
Role: Dataset Creator & Curator

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If you use this dataset in your work, feel free to cite it or reach out with feedback and contributions!