README.md

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

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

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