Update README.md

README.md

@@ -10,4 +10,42 @@ datasets:
 # inference: true
 # 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) 
+
+```