 Usage
 ```python
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 
 model_name = 'doc2query/stackexchange-title-body-t5-small-v1'
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 model = T5ForConditionalGeneration.from_pretrained(model_name)
 
 text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects."
 
 
 input_ids = tokenizer.encode(text, max_length=384, truncation=True, return_tensors='pt')
 outputs = model.generate(
     input_ids=input_ids,
     max_length=64,
     do_sample=True,
     top_p=0.95,
     num_return_sequences=5)
 
 print("Text:")
 print(text)
 
 print("\nGenerated Queries:")
 for i in range(len(outputs)):
     query = tokenizer.decode(outputs[i], skip_special_tokens=True)
     print(f'{i + 1}: {query}')
 ```
 
 **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it.
 
 