bniladridas/conversational-ai-base-model

Conversational AI Base Model

🤖 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

pip install transformers torch

Usage Example

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

@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
• Hugging Face: @bniladridas

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Last Updated: February 2025