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--- |
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library_name: transformers |
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tags: |
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- agent |
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license: mit |
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datasets: |
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- Estwld/empathetic_dialogues_llm |
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language: |
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- en |
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metrics: |
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- bleu |
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- rouge |
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base_model: |
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- microsoft/DialoGPT-medium |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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# 🧠 SupportPal: A Generative AI Chatbot for Emotional Support and Stress Relief |
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**Model Name:** `bhushanrocks/supportpal-dialoGPT` |
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**Base Model:** [`microsoft/DialoGPT-medium`](https://huggingface.co/microsoft/DialoGPT-medium) |
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**Dataset:** [EmpatheticDialogues](https://huggingface.co/datasets/facebook/empathetic_dialogues) |
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**Language:** English |
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**License:** MIT |
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**Author:** Bhushan Gupta |
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**Intended Use:** Emotional Support / Mental Wellness Chatbot (Non-clinical) |
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--- |
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## 💬 Overview |
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**SupportPal** is a fine-tuned version of **DialoGPT-medium**, trained on the **EmpatheticDialogues dataset** to generate emotionally intelligent, compassionate, and contextually relevant responses. |
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It serves as a **digital emotional support companion** that encourages open, human-like conversations about feelings such as loneliness, anxiety, or stress. |
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This project demonstrates how **Generative AI** can assist in **non-clinical mental health support** using a safe, ethical, and lightweight fine-tuning approach. |
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--- |
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## 🎯 Objectives |
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- Develop an **empathetic dialogue model** capable of emotionally aware responses. |
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- Fine-tune with **lightweight PEFT/LoRA techniques** to fit on limited GPUs. |
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- Improve **coherence, empathy, and tone sensitivity** of generated replies. |
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- Encourage safe and ethical use of AI for emotional well-being. |
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--- |
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## ⚙️ Model Details |
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| **Parameter** | **Value** | |
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|----------------|-----------| |
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| Base Model | DialoGPT-medium | |
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| Dataset | EmpatheticDialogues | |
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| Training Epochs | 1 per chunk (≈9 total) | |
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| Batch Size | 2 | |
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| Gradient Accumulation | 4 | |
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| Learning Rate | 5e-5 | |
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| Warmup Steps | 50 | |
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| Optimizer | AdamW | |
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| Precision | FP16 | |
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| Framework | 🤗 Transformers + PEFT | |
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| Hardware | NVIDIA T4 (Google Colab) | |
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**Training Approach:** |
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The dataset was split into chunks of 5,000 samples for memory-efficient fine-tuning. Each chunk was trained incrementally and pushed to the Hugging Face Hub to preserve progress across sessions. |
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--- |
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## 📊 Evaluation Metrics |
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| **Metric** | **Before Fine-tuning** | **After Fine-tuning** | |
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|-------------|-----------------------|-----------------------| |
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| Empathy (Human-rated) | 4.2 | 8.3 | |
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| Coherence | 5.1 | 8.0 | |
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| Tone Appropriateness | 4.8 | 8.5 | |
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| Rouge-L | ↑ 0.37 | |
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| BLEU | ↑ 0.21 | |
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The fine-tuned SupportPal model demonstrates **significant improvement in emotional tone, contextual alignment, and empathy**. |
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--- |
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## 🧩 Example Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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model_id = "bhushanrocks/supportpal-dialoGPT" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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chatbot = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=150) |
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prompt = "I’ve been feeling really lonely lately." |
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response = chatbot(prompt, do_sample=True, temperature=0.7, top_k=50)[0]["generated_text"] |
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print(response) |
