---
language:
- en
tags:
- gpt2
license: mit
datasets:
- wiki_qa
# pipeline_tag: conversational
inference: false
# widget:
# - text: 'What are Glaciers?'
---
## Description
This Question-Answering model was fine-tuned & trained from a generative, left-to-right transformer in the style of GPT-2, the [distilgpt2](https://huggingface.co/distilgpt2) model. This model was trained on [Wiki-QA](https://huggingface.co/datasets/wiki_qa) dataset from Microsoft.

# How to run Distil-GPT2-Wiki-QA using Transformers
## Question-Answering

The following code shows how to use the Distil-GPT2-Wiki-QA checkpoint and Transformers to generate Answers.
```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer

import torch
import re

tokenizer = GPT2Tokenizer.from_pretrained("XBOT-RK/distilgpt2-wiki-qa")
model = GPT2LMHeadModel.from_pretrained("XBOT-RK/distilgpt2-wiki-qa")

device = "cuda" if torch.cuda.is_available() else "cpu"

def infer(question):
    generated_tensor = model.generate(**tokenizer(question, return_tensors="pt").to(device), max_new_tokens = 50)
    generated_text = tokenizer.decode(generated_tensor[0])
    return generated_text

def processAnswer(question, result):
    answer = result.replace(question, '').strip()
    if "<bot>:" in answer:
        answer = re.search('<bot>:(.*)', answer).group(1).strip()
    if "<endofstring>" in answer:
        answer = re.search('(.*)<endofstring>', answer).group(1).strip()
    return answer

question = "What is a tropical cyclone?"
result = infer(question)
answer = processAnswer(question, result)
print('Question: ', question)
print('Answer: ', answer) 

```