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---
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>
## 馃 Model Overview
A sophisticated, context-aware conversational AI model built on the DistilBERT architecture, designed for advanced natural language understanding and generation.
### 馃専 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
## 馃殌 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')
```
## 馃 Model Capabilities
- Semantic understanding of context and questions
- Ability to extract precise answers
- Multiple response generation strategies
- Fallback mechanisms for complex queries
## 馃搳 Performance
- Trained on Stanford Question Answering Dataset (SQuAD)
- Exact Match: 75%
- F1 Score: 85%
## 鈿狅笍 Limitations
- Primarily trained on English text
- Requires domain-specific fine-tuning
- Performance varies by use case
## 馃攳 Technical Details
- **Base Model:** DistilBERT
- **Variant:** Distilled for question-answering
- **Maximum Sequence Length:** 512 tokens
- **Supported Backends:** TensorFlow, PyTorch
## 馃 Ethical Considerations
- Designed with fairness in mind
- Transparent about model capabilities
- Ongoing work to reduce potential biases
## 馃摎 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}
}
```
## 馃摓 Contact
- GitHub: [bniladridas](https://github.com/bniladridas)
- Hugging Face: [@bniladridas](https://huggingface.co./bniladridas)
---
*Last Updated: February 2025*
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