include ../../_includes/_mixins

p By default spaCy loads a #[code data/vocab/vec.bin] file, where the #[em data] directory is within the #[code spacy.en] module directory.  This file can be replaced, to customize the word vectors that spaCy loads. You can also replace the word vectors at run-time.

+h3("replacing-vec-bin") Replacing vec.bin

p The function #[code spacy.vocab.write_binary_vectors] creates a word vectors file in spaCy's binary data format. It expects a #[code bz2] file in the following format:

+code.
    word_key1 0.92 0.45 -0.9 0.0
    word_key2 0.3 0.1 0.6 0.3
    ...

p That is, each line is a single entry. Each entry consists of a key string, followed by a sequence of floats. Each entry should have the same number of floats.

p The following example script will replace the #[code vec.bin] file with vectors read from a #[code bz2] archive:

+code.
    from spacy.vocab import write_binary_vectors
    import spacy.en
    from os import path

    def main(bz2_loc, bin_loc=None):
            if bin_loc is None:
                    bin_loc = path.join(path.dirname(spacy.en.__file__), 'data', 'vocab', 'vec.bin')
            write_binary_vectors(bz2_loc, bin_loc)

    if __name__ == '__main__':
            plac.call(main)

+h3("replace-vectors-archive") Replace the Vectors at Run-Time, From an Archive

p Since v0.93, instances of #[code Vocab] allow new vectors to be loaded from #[code bz2] archive files. This allows vectors to be loaded as follows:

+code.
    >>> from spacy.en import English
    >>> nlp = English()
    >>> n_dimensions = nlp.vocab.load_vectors('glove.840B.300d.txt.bz2')
    >>> n_dimensions
    300

+h3("replace-vectors-per-word") Replace Vectors at Run-Time, Per Word

p Since v0.93, you can assign to the #[code .vector] attribute of #[code Lexeme] instances. Tokens of that lexical type will then inherit the updated vector. For instance:

+code.
    >>> from spacy.en import English
    >>> nlp = English()
    >>> apples, oranges = nlp(u'apples oranges')
    <type 'spacy.tokens.token.Token'>
    >>> apples_lexeme = nlp.vocab[u'apples']
    >>> type(apples), type(apples_lexeme)
    (<type 'spacy.tokens.token.Token'>, <type 'spacy.lexeme.Lexeme'>)
    >>> sum(apples.vector)
    0.56299778164247982
    >>> apples_lexeme.vector *= 2
    >>> sum(apples.vector)
    1.1259955632849596

p All tokens which have the #[code orth] attribute #[em apples] will inherit the updated vector.

p Note that the updated vectors won't persist after exit, unless you persist them yourself, and then replace the #[code vec.bin] file as described above.

p A popular source of word vectors are the #[a(href="http://nlp.stanford.edu/projects/glove/" target="_blank") GloVe word vectors], particularly those calculated off the #[a(href="https://commoncrawl.org/" target="_blank") Common Crawl]. Note that the provided vector file has a few entries which are not valid UTF8 strings. These should be filtered out.

p Future versions of spaCy will allow you to provide a file-like object, instead of a location of a #[bz2] file.