## 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 XBOT-RK/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)

# Output

"Question:  What is a tropical cyclone?"
"Answer:  The cyclone is named after the climber Edmond Halley, who described it as the 'most powerful cyclone of the Atlantic'."

```
{"language": ["en"], "license": "mit", "tags": ["gpt2"], "datasets": ["wiki_qa"], "inference": false}
transformers pytorch safetensors gpt2 text-generation en dataset:wiki_qa license:mit autotrain_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #en #dataset-wiki_qa #license-mit #autotrain_compatible #text-generation-inference #region-us 

## Description
This Question-Answering model was fine-tuned & trained from a generative, left-to-right transformer in the style of GPT-2, the distilgpt2 model. This model was trained on Wiki-QA dataset from Microsoft.

# How to run XBOT-RK/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.

## Description
This Question-Answering model was fine-tuned & trained from a generative, left-to-right transformer in the style of GPT-2, the distilgpt2 model. This model was trained on Wiki-QA dataset from Microsoft. # How to run XBOT-RK/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.
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #en #dataset-wiki_qa #license-mit #autotrain_compatible #text-generation-inference #region-us 
 ## Description
This Question-Answering model was fine-tuned & trained from a generative, left-to-right transformer in the style of GPT-2, the distilgpt2 model. This model was trained on Wiki-QA dataset from Microsoft. # How to run XBOT-RK/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.
TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #en #dataset-wiki_qa #license-mit #autotrain_compatible #text-generation-inference #region-us 
## Description
This Question-Answering model was fine-tuned & trained from a generative, left-to-right transformer in the style of GPT-2, the distilgpt2 model. This model was trained on Wiki-QA dataset from Microsoft.# How to run XBOT-RK/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.