I am playing around with summarization, following this tutorial. Instead of using the cnndm dataset, I am just copying the text of a news article from the internet, so how should I format this string to be interpreted by the T5 model. This is what I have tried so far:

```
torch.set_default_tensor_type('torch.cuda.FloatTensor')
device = torch.device("cuda")
padding_idx = 0
eos_idx = 1
max_seq_len = 512
t5_sp_model_path = "https://download.pytorch.org/models/text/t5_tokenizer_base.model"
transform = T5Transform(
sp_model_path=t5_sp_model_path,
max_seq_len=max_seq_len,
eos_idx=eos_idx,
padding_idx=padding_idx,
)
t5_base = T5_BASE_GENERATION
transform = t5_base.transform()
transform = transform.to(device)
model = t5_base.get_model()
model.eval()
model = model.to(device)
sequence_generator = GenerationUtils(model)
sequence_generator.device = device
beam_size = 1
model_input = transform("summarize: " + text_of_article)
model_output = sequence_generator.generate(model_input, eos_idx=eos_idx, num_beams=beam_size)
output_text = transform.decode(model_output.tolist())
```

When I do this, I get the error:

```
AssertionError: For batched (3-D) `query`, expected `key` and `value` to be 3-D but found 2-D and 2-D tensors respectively
```

which I assume is because I haven’t inputted the data correctly? The tutorial mentions that the T5 model requires data to be batched, but I don’t see how my input is any different than what is in the tutorial:

```
batch = next(iter(cnndm_dataloader))
input_text = batch["article"]
target = batch["abstract"]
beam_size = 1
model_input = transform(input_text)
```

It looks like they are just getting the next article (prefixed with the task) from the dataloader. I am new to pytorch and nlp in general, so any help would be greatly appreciated!