Update README.md

README.md

@@ -1 +1,61 @@
-#T5 Base Fine-Tuned on SQuAD for Question Generation
+---
+language: en
+datasets:
+- squad
+widget:
+ - text: "<answer> T5 <context> Cheng fine-tuned T5 on SQuAD for question generation."
+   example_title: "Example 1"
+ - text: "<answer> SQuAD <context> Cheng fine-tuned T5 on SQuAD dataset for question generation."
+   example_title: "Example 2"
+ - text: "<answer> deep learning <context> Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning."
+   example_title: "Example 3"
+---
+
+# T5-Base Fine-Tuned on SQuAD for Question Generation
+
+### Model in Action:
+
+```python
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+trained_model_path = 'ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation'
+trained_tokenizer_path = 'ZhangCheng/T5-Base-Fine-Tuned-for-Question-Generation'
+
+class QuestionGeneration:
+
+    def __init__(self, model_dir=None):
+        self.model = T5ForConditionalGeneration.from_pretrained(trained_model_path)
+        self.tokenizer = T5Tokenizer.from_pretrained(trained_tokenizer_path)
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.model = self.model.to(self.device)
+
+    def generate(self, answer:str, context:str):
+        input_text = '<answer> %s <context> %s ' % (answer, context)
+        encoding = self.tokenizer.encode_plus(
+            input_text,
+            return_tensors='pt'
+        )
+        input_ids = encoding['input_ids'].to(self.device)
+        attention_mask = encoding['attention_mask'].to(self.device)
+        self.model.eval()
+        beam_outputs = self.model.generate(
+            input_ids = input_ids,
+            attention_mask = attention_mask
+        )
+        question = self.tokenizer.decode(
+            beam_outputs[0],
+            skip_special_tokens = True,
+            clean_up_tokenization_spaces = True
+        )
+        return {'question': question, 'answer': answer}
+
+if __name__ == "__main__":
+    context = 'ZhangCheng fine-tuned T5 on SQuAD dataset for question generation.'
+    answer = 'ZhangCheng'
+    QG = QuestionGeneration()
+    qa = QG.generate(answer, context)
+    print(qa['question'])
+    # Output: 
+    # Who fine-tuned T5 on SQuAD dataset for question generation?
+```