ai-chatbot2

Model Card for Fine-Tuned GPT-2

Model Overview

This repository contains a fine-tuned version of the GPT-2 model. The model has been adapted to provide a conversational AI experience, suitable for chatbots and similar applications. It is designed to generate human-like text based on the input it receives.

Model Details

• Model Type: Transformer-based language model
• Base Model: GPT-2
• Version: 1.0
• Architecture: 12-layer transformer with 768 hidden units and 12 attention heads.

Intended Use

The model is intended for use in creating chatbots and conversational agents. It can generate responses to user inputs in a coherent and contextually relevant manner.

Examples of Use Cases

• Customer support chatbots
• Interactive storytelling applications
• Personal assistants

Training Data

The model was fine-tuned on a diverse dataset that includes:

• Dialogue from various sources, such as social media, forums, and chat transcripts.
• Text data representative of conversational exchanges.

The dataset used for fine-tuning has been curated to ensure a wide range of topics and conversational styles.

Limitations

• The model may generate incorrect or nonsensical answers in some situations.
• It can exhibit biases present in the training data. Care should be taken to ensure ethical use, particularly in sensitive applications.
• It may not understand highly specialized or technical topics unless trained on specific datasets.

How to Use

You can use this model via the Hugging Face Transformers library. Below is a sample code snippet to get you started:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load the fine-tuned model and tokenizer
model = GPT2LMHeadModel.from_pretrained("sohail2332/ai-chatbot2")
tokenizer = GPT2Tokenizer.from_pretrained("sohail2332/ai-chatbot2")

# Generate text
input_text = "Hello, how can I help you today?"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate a response
output = model.generate(input_ids, max_length=50, num_return_sequences=1)
response = tokenizer.decode(output[0], skip_special_tokens=True)

print(response)