T5_INPUTS_DOCSTRING correct!?
Is this docstring even correct?

The [CLS] and [SEP] Tokens do not appear in the from google provided dictionaries for the pretrained "t5-base" model. If this is true could you then please provide a correct example of how to use the text generation feature of this almighty transformer? (BoolQ or QA) That would help me a lot.

Thanks!

```
T5_INPUTS_DOCSTRING = r"""
    Inputs:
        **input_ids**: ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
            Indices of input sequence tokens in the vocabulary.
            To match pre-training, T5 input sequence should be formatted with [CLS] and [SEP] tokens as follows:
            (a) For sequence pairs:
                ``tokens:         [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
            (b) For single sequences:
                ``tokens:         [CLS] the dog is hairy . [SEP]``
            T5 is a model with relative position embeddings so you should be able to pad the inputs on
            the right or the left.
            Indices can be obtained using :class:`transformers.T5Tokenizer`.
            See :func:`transformers.PreTrainedTokenizer.encode` and
            :func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
        **attention_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(batch_size, sequence_length)``:
            Mask to avoid performing attention on padding token indices.
            Mask values selected in ``[0, 1]``:
            ``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
        **head_mask**: (`optional`) ``torch.FloatTensor`` of shape ``(num_heads,)`` or ``(num_layers, num_heads)``:
            Mask to nullify selected heads of the self-attention modules.
            Mask values selected in ``[0, 1]``:
            ``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
"""

```
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