distillgpt2_accountant

🚀 Fine-Tuned GPT-2 for Question Answering

🌟 Model Overview

Welcome to the Fine-Tuned GPT-2 QA Model! This model is a specialized version of GPT-2, fine-tuned on a high-quality question-answering dataset to provide accurate and concise answers to a wide range of questions. Whether you're building a chatbot, a knowledge base, or an interactive QA system, this model is designed to deliver reliable responses with the power of the Hugging Face transformers library.

🔑 Key Features:

• 📚 Fine-tuned for question-answering tasks
• 🤖 Based on the GPT-2 architecture
• ⚡ Supports GPU acceleration for faster inference
• 🌐 Hosted on Hugging Face Hub for easy access

🛠️ Usage

Get started with this model in just a few lines of code! Below is an example of how to load the model and tokenizer using the transformers library and perform question answering.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ShahDhruv/distillgpt2_accountant")
model = AutoModelForCausalLM.from_pretrained("ShahDhruv/distillgpt2_accountant")

question = "What is double-entry bookkeeping?"

input_text = f"Question: {question}\nAnswer: "
inputs = tokenizer(input_text, return_tensors="pt")

# Generate answer
outputs = model.generate(
    **inputs,
    max_length=200,
    num_return_sequences=1,
    do_sample=True,
    temperature=0.4,
    top_k=40,
    top_p=0.95,
    repetition_penalty=1.2,
    min_length = 50,
    length_penalty = 1.2
)
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(answer)