TokenClassifier model #3203
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TokenClassifier model #3203
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| from flair.embeddings import TokenEmbeddings | ||
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| log = logging.getLogger("flair") | ||
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| class WordTagger(flair.nn.DefaultClassifier[Sentence, Token]): | ||
| class TokenClassifier(flair.nn.DefaultClassifier[Sentence, Token]): | ||
| """This is a simple class of models that tags individual words in text.""" |
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I think we shouldn't just remove the WordTagger as this breaks models using the WordTagger (Especially if they are part of a MultiTaskModel).
I would suggest, that we use a DeprecationHelper like specified here, but specify the version when we delete (let's say 0.14.0; incrementing by 2.)
That way we could give users a chance to upgrade between multiple versions.
We can also discuss this further in private
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Thanks, good idea!
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This PR introduces the
TokenClassifierclass, a renamed and extended version ofWordTagger. It directly inherits fromDefaultClassifierand should be used for all token-level prediction tasks that do not require an LSTM-CRF decoder (for such tasks, theSequenceTaggershould be used).The main idea is to offer a model that inherits from
DefaultClassifierfor each label type we predict, i.e.:TokenClassifierfor predictingTokenlabelsTextPairClassifierfor predictingTextPairlabelsRelationClassifierfor predictingRelationlabelsSpanClassifierfor predictingSpanlabels (this class is currently calledEntityLinkerand should be renamed)TextClassifierfor predictingSentencelabels (might need to be renamed to SentenceClassifier)An advantage of such a structure is that most functionality (such as new decoders) needs to only be implemented once in
DefaultClassifierand then is immediately usable for all model classes.Edit: This class also changes the default behavior of the make_label_dictionary method. The UNK token is no longer automatically added to a dictionary. We now skip unknown labels to handle loss computation in such cases.