nielsr:

However, there’s one additional challenge, in the sense that models like BERT operate on subword tokens, rather than words, meaning that a word like “hello” might be tokenized into [“hel”, “lo”]. This means that one should actually labels all tokens rather than all words, as BERT will be trained to predict a label for every token. There are multiple strategies here, one could either propagate the label to all subtokens of a word, or only label the first subword token of a given word.


I didn’t understand this, plz can you explain with an example as well about strategies.