create a lstm model using the word2vec embedding in matlab

main.m
word2VecModel = word2vecReader('path/to/word2vec/model.bin');
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main.m
words = ["word1", "word2", "word3", ...];
% get the embeddings for each word
word_embeddings = word2vecModel.getVectors(words);
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main.m
textData = readtable('path/to/text/data.csv');
% preprocess the text data
documents = preprocessTextData(textData);
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main.m
% create a word embedding object
embeddingDimension = size(word_embeddings, 2);
wordEmbeddingObj = wordEmbedding(words, word_embeddings);
% Convert the preprocessed documents into sequence data
docsSequences = doc2sequence(wordEmbeddingObj,documents);
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main.m
inputSize = embeddingDimension;
numHiddenUnits = 100;
numClasses = length(categories(textData.Label));
layers = [
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer
    ];
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main.m
options = trainingOptions('adam', ...
    'MaxEpochs',50, ...
    'MiniBatchSize',64, ...
    'Shuffle','every-epoch', ...
    'Verbose', false);
[net,~] = trainNetwork(docsSequences',textData.Label,layers,options);
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main.m
testData = readtable('path/to/test/data.csv');
% preprocess the test text data
testDocuments = preprocessTextData(testData);
% Convert the preprocessed test documents into sequence data
testSequences = doc2sequence(wordEmbeddingObj,testDocuments);
% Test the LSTM model on the test sequence data
[testPredictions, scores] = classify(net,testSequences');
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