BytePairEmbeddings are word embeddings that are precomputed on the subword-level. This means that they are able to
embed any word by splitting words into subwords and looking up their embeddings. BytePairEmbeddings were proposed
and computed by Heinzerling and Strube (2018) who found that they offer nearly the same accuracy as word embeddings, but at a fraction
of the model size. So they are a great choice if you want to train small models.
You initialize with a language code (275 languages supported), a number of 'syllables' (one of ) and a number of dimensions (one of 50, 100, 200 or 300). The following initializes and uses byte pair embeddings for English:
from flair.embeddings import BytePairEmbeddings
# init embedding
embedding = BytePairEmbeddings('en')
# create a sentence
sentence = Sentence('The grass is green .')
# embed words in sentence
embedding.embed(sentence)More information can be found on the byte pair embeddings web page.
BytePairEmbeddings also have a multilingual model capable of embedding any word in any language.
You can instantiate it with:
# init embedding
embedding = BytePairEmbeddings('multi')You can also load custom BytePairEmbeddings by specifying a path to model_file_path and embedding_file_path arguments. They correspond respectively to a SentencePiece model file and to an embedding file (Word2Vec plain text or GenSim binary). For example:
# init custom embedding
embedding = BytePairEmbeddings(model_file_path='your/path/m.model', embedding_file_path='your/path/w2v.txt')