metadata
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
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}
}