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CHARACTER_EMBEDDINGS.md

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Character Embeddings

CharacterEmbeddings allow you to add character-level word embeddings during model training. Note that these embeddings are randomly initialized when you initialize the class, so they are not meaningful unless you train them on a specific downstream task.

For instance, the standard sequence labeling architecture used by Lample et al. (2016) is a combination of classic word embeddings with task-trained character features. Normally this would require you to implement a hierarchical embedding architecture in which character-level embeddings for each word are computed using an RNN and then concatenated with word embeddings.

In Flair, we simplify this by treating CharacterEmbeddings just like any other embedding class. To reproduce the Lample architecture, you need only combine them with standard WordEmbeddings in an embedding stack:

# init embedding stack
embedding = StackedEmbeddings(
    [
        # standard word embeddings
        WordEmbeddings('glove'),

        # character-level features
        CharacterEmbeddings(),
    ]
)

If you pass this stacked embedding to a train method, the character-level features will now automatically be trained for your downstream task.