Load the factory reports data and create a tokenizedDocument array.

filename = "factoryReports.csv";
data = readtable(filename,'TextType','string');
textData = data.Description;
documents = tokenizedDocument(textData);

Create a word encoding.

enc = wordEncoding(documents);

Convert the documents to sequences of word indices.

sequences = doc2sequence(enc,documents);

View the sizes of the first 10 sequences. Each sequence is a 1-by-S vector, where S is the number of word indices in the sequence. Because the sequences are padded, S is constant.

sequences(1:10)

ans=10×1 cell array
    {[           0 0 0 0 0 0 0 1 2 3 4 5 6 7 8 9 10]}
    {[  0 0 0 0 0 0 11 12 13 14 15 2 16 17 18 19 10]}
    {[    0 0 0 0 0 0 20 2 21 22 7 23 24 25 7 26 10]}
    {[        0 0 0 0 0 0 0 0 0 0 0 27 28 6 7 18 10]}
    {[        0 0 0 0 0 0 0 0 0 0 0 0 29 30 7 31 10]}
    {[    0 0 0 0 0 0 0 32 33 6 7 34 35 36 37 38 10]}
    {[      0 0 0 0 0 0 0 0 0 39 40 36 41 6 7 42 10]}
    {[    0 0 0 0 0 0 0 0 43 44 22 45 46 47 7 48 10]}
    {[      0 0 0 0 0 0 0 0 0 0 49 50 17 7 51 48 10]}
    {[0 0 0 0 52 8 53 36 54 55 56 57 58 59 22 60 10]}

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}

Convert a collection of documents to sequences of word vectors using a pretrained word embedding, and pad or truncate the sequences to a specified length.

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;

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. Specify to left-pad or truncate the sequences to have length 100.

sequences = doc2sequence(emb,documents,'Length',100);

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 (the sequence length). Because the sequence length is specified, S is constant.

sequences(1:10)

ans=10×1 cell array
    {300×100 single}
    {300×100 single}
    {300×100 single}
    {300×100 single}
    {300×100 single}
    {300×100 single}
    {300×100 single}
    {300×100 single}
    {300×100 single}
    {300×100 single}