@@ -121,7 +121,7 @@ As you saw in the example `tokens` field above, it looks like the input has alre
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@@ -121,7 +121,7 @@ As you saw in the example `tokens` field above, it looks like the input has alre
However, this adds some special tokens `[CLS]` and `[SEP]` and the subword tokenization creates a mismatch between the input and labels. A single word corresponding to a single label may now be split into two subwords. You'll need to realign the tokens and labels by:
However, this adds some special tokens `[CLS]` and `[SEP]` and the subword tokenization creates a mismatch between the input and labels. A single word corresponding to a single label may now be split into two subwords. You'll need to realign the tokens and labels by:
1. Mapping all tokens to their corresponding word with the [`word_ids`](https://huggingface.co/docs/tokenizers/python/latest/api/reference.html#tokenizers.Encoding.word_ids) method.
1. Mapping all tokens to their corresponding word with the [`word_ids`](https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.BatchEncoding.word_ids) method.
2. Assigning the label `-100` to the special tokens `[CLS]` and `[SEP]` so they're ignored by the PyTorch loss function.
2. Assigning the label `-100` to the special tokens `[CLS]` and `[SEP]` so they're ignored by the PyTorch loss function.
3. Only labeling the first token of a given word. Assign `-100` to other subtokens from the same word.
3. Only labeling the first token of a given word. Assign `-100` to other subtokens from the same word.
