---
language: en
license: mit
tags:
- conversational-ai
- question-answering
- nlp
- transformers
- context-aware
datasets:
- squad
metrics:
- exact_match
- f1_score
model-index:
- name: Conversational AI Base Model
  results:
  - task: 
      type: question-answering
    dataset: 
      name: squad
      type: question-answering
    metrics:
      - type: exact_match
        value: 0.75
      - type: f1_score
        value: 0.85
---

# Conversational AI Base Model

<p align="center">
  <a href="https://huggingface.co./bniladridas/conversational-ai-base-model">
    <img src="https://huggingface.co./front/assets/huggingface_logo-noborder.svg" width="200" alt="Hugging Face">
  </a>
</p>

## 🤖 Model Overview

A sophisticated, context-aware conversational AI model built on the DistilBERT architecture, designed for advanced natural language understanding and generation.

### 🌟 Key Features
- **Advanced Response Generation**
  - Multi-strategy response mechanisms
  - Context-aware conversation tracking
  - Intelligent fallback responses

- **Flexible Architecture**
  - Built on DistilBERT base model
  - Supports TensorFlow and PyTorch
  - Lightweight and efficient

- **Robust Processing**
  - 512-token context window
  - Dynamic model loading
  - Error handling and recovery

## 🚀 Quick Start

### Installation
```bash
pip install transformers torch
```

### Usage Example
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer

# Load model and tokenizer
model = AutoModelForQuestionAnswering.from_pretrained('bniladridas/conversational-ai-base-model')
tokenizer = AutoTokenizer.from_pretrained('bniladridas/conversational-ai-base-model')
```

## 🧠 Model Capabilities
- Semantic understanding of context and questions
- Ability to extract precise answers
- Multiple response generation strategies
- Fallback mechanisms for complex queries

## 📊 Performance
- Trained on Stanford Question Answering Dataset (SQuAD)
- Exact Match: 75%
- F1 Score: 85%

## ⚠️ Limitations
- Primarily trained on English text
- Requires domain-specific fine-tuning
- Performance varies by use case

## 🔍 Technical Details
- **Base Model:** DistilBERT
- **Variant:** Distilled for question-answering
- **Maximum Sequence Length:** 512 tokens
- **Supported Backends:** TensorFlow, PyTorch

## 🤝 Ethical Considerations
- Designed with fairness in mind
- Transparent about model capabilities
- Ongoing work to reduce potential biases

## 📚 Citation
```bibtex
@misc{conversational-ai-model,
  title={Conversational AI Base Model},
  author={Niladri Das},
  year={2025},
  url={https://huggingface.co./bniladridas/conversational-ai-base-model}
}
```

## 📞 Contact
- GitHub: [bniladridas](https://github.com/bniladridas)
- Hugging Face: [@bniladridas](https://huggingface.co./bniladridas)

---

*Last Updated: February 2025*