seq2seq: Replace the embeddings with pre-trained word embeddings such as word2vec

Hi,
Thank you for your tutorial! I tried to change the embedding with pre-trained word embeddings such as word2vec, here is my code:

class Lang:
    def __init__(self, name):
        self.name = name
        self.word2index = {}
        self.word2count = {}
        self.index2word = {0: "SOS", 1: "EOS"}
        self.n_words = 2  # Count SOS and EOS

    def get_word2vec(self):
        word2vec = KeyedVectors.load_word2vec_format('Models/Word2Vec/wiki.he.vec')
        return word2vec
    
    def addSentence(self, sentence):
        for word in sentence.split(' '):
            self.addWord(word)

    def addWord(self, word):
        if word not in self.word2index:
            self.word2index[word] = self.get_word2vec[word]
            self.word2count[word] = 1
            self.index2word[self.n_words] = word
            self.n_words += 1
        else:
            self.word2count[word] += 1

the dimension size of this word2vec is 300 dimensions
Is I need to change other things in my Encoder?

Thank you!

Start with the Lang class and the Encoder referenced in the issue, and review how the current embedding dimensions and word indexes are used. The issue does not name a file or test, and completion would require defining which encoder and data-pipeline changes are needed for pre-trained word embeddings.