Create README.md

README.md

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+---
+license: afl-3.0
+---
+
+# Question generation using T5 transformer trained on SQuAD
+
+<h2> <i>Input format: context: "..." answer(optional): "..." </i></h2>
+
+Import the pretrained model as well as tokenizer:
+```
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+model = T5ForConditionalGeneration.from_pretrained('AbhilashDatta/T5_qgen-squad_v2') 
+tokenizer = T5Tokenizer.from_pretrained('AbhilashDatta/T5_qgen-squad_v2')
+```
+
+Then use the tokenizer to encode/decode and model to generate: 
+
+```
+input = "context: My name is Abhilash Datta. answer: Abhilash"
+batch = tokenizer(input, padding='longest', max_length=512, return_tensors='pt')
+inputs_batch = batch['input_ids'][0]
+inputs_batch = torch.unsqueeze(inputs_batch, 0)
+
+ques_id = model.generate(inputs_batch, max_length=100, early_stopping=True)
+ques_batch = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in ques_id]
+
+print(ques_batch)
+```
+
+Output:
+```
+['what is my name']
+```