  summarization models (such as Bart or T5).
token: a part of a sentence, usually a word, but can also be a subword (non-common words are often split in subwords)
  or a punctuation symbol.
transformer: self-attention based deep learning model architecture.
Model inputs
Every model is different yet bears similarities with the others. Therefore most models use the same inputs, which are
detailed here alongside usage examples.
Input IDs
The input ids are often the only required parameters to be passed to the model as input. They are token indices,
numerical representations of tokens building the sequences that will be used as input by the model.
Each tokenizer works differently but the underlying mechanism remains the same. Here's an example using the BERT
tokenizer, which is a WordPiece tokenizer:
