pretrained fasttext word embedding -凯发k8网页登录

download and install the text analytics toolbox model for fasttext english 16 billion token word embedding support package.

fasttextwordembedding

emb = fasttextwordembedding

emb = 
  wordembedding with properties:
     dimension: 300
    vocabulary: [1×1000000 string]

load a pretrained word embedding using fasttextwordembedding. this function requires text analytics toolbox™ model for fasttext english 16 billion token word embedding support package. if this support package is not installed, then the function provides a download link.

emb = fasttextwordembedding

emb = 
  wordembedding with properties:
     dimension: 300
    vocabulary: [1×1000000 string]

map the words "italy", "rome", and "paris" to vectors using word2vec.

italy = word2vec(emb,"italy");
rome = word2vec(emb,"rome");
paris = word2vec(emb,"paris");

map the vector italy - rome paris to a word using vec2word.

word = vec2word(emb,italy - rome   paris)

word = 
"france"

convert an array of tokenized documents to sequences of word vectors using a pretrained word embedding.

load a pretrained word embedding using the fasttextwordembedding function. this function requires text analytics toolbox™ model for fasttext english 16 billion token word embedding support package. if this support package is not installed, then the function provides a download link.

emb = fasttextwordembedding;

load the factory reports data and create a tokenizeddocument array.

filename = "factoryreports.csv";
data = readtable(filename,'texttype','string');
textdata = data.description;
documents = tokenizeddocument(textdata);

convert the documents to sequences of word vectors using doc2sequence. the doc2sequence function, by default, left-pads the sequences to have the same length. when converting large collections of documents using a high-dimensional word embedding, padding can require large amounts of memory. to prevent the function from padding the data, set the 'paddingdirection' option to 'none'. alternatively, you can control the amount of padding using the 'length' option.

sequences = doc2sequence(emb,documents,'paddingdirection','none');

view the sizes of the first 10 sequences. each sequence is d-by-s matrix, where d is the embedding dimension, and s is the number of word vectors in the sequence.

sequences(1:10)

ans=10×1 cell array
    {300×10 single}
    {300×11 single}
    {300×11 single}
    {300×6  single}
    {300×5  single}
    {300×10 single}
    {300×8  single}
    {300×9  single}
    {300×7  single}
    {300×13 single}