

Use case 3: token classification (training), parse_html=False
For token classification tasks (such as SWDE), one can also provide the
corresponding node labels in order to train a model. The processor will then convert these into token-level labels.
By default, it will only label the first wordpiece of a word, and label the remaining wordpieces with -100, which is the
ignore_index of PyTorch's CrossEntropyLoss. In case you want all wordpieces of a word to be labeled, you can
initialize the tokenizer with only_label_first_subword set to False.
thon