harpertoken/harpertokenConvAI

Harpertoken ConvAI

Model Overview

A context-aware conversational AI model based on DistilBERT for 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('harpertoken/harpertokenConvAI')
tokenizer = AutoTokenizer.from_pretrained('harpertoken/harpertokenConvAI')

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

Citation

@misc{harpertoken-convai,
  title={Harpertoken ConvAI},
  author={Niladri Das},
  year={2025},
  url={https://huggingface.co/harpertoken/harpertokenConvAI}
}