Use custom Word2Vec embedding instead of GloVe - keras

Question

I am working on a text generation using seq2seq model where GloVe embedding is being used. I want to use a custom Word2Vec (CBOW/Gensim) embedding in this code. Can anyone please help to use my custom embedding instead of GloVe?
def initialize_embeddings(self):
"""Reads the GloVe word-embeddings and creates embedding matrix and word to index and index to word mapping."""
# load the word embeddings
self.word2vec = {}
with open(glove_path%self.EMBEDDING_DIM, 'r') as file:
for line in file:
vectors = line.split()
self.word2vec[vectors[0]] = np.asarray(vectors[1:], dtype="float32")```
```# get the embeddings matrix
self.num_words = min(self.MAX_VOCAB_SIZE, len(self.word2idx)+1)
self.embeddings_matrix = np.zeros((self.num_words, self.EMBEDDING_DIM))
for word, idx in self.word2idx.items():
if idx <= self.num_words:
word_embeddings = self.word2vec.get(word)
if word_embeddings is not None:
self.embeddings_matrix[idx] = word_embeddings
self.idx2word = {v:k for k,v in self.word2idx.items()}
This code is for GloVe embedding which is transformed to Word2Vec. I want to load my own Word2Vec embedding.

Answer 1

word2vec and Glove are a techniques for producing word embeddings, i.e., for modelling text (a set of sentences) into computer-readable vectors.
While word2vec trains on the local context (neighboring words), Glove will look for words co-occurrence in a whole text or corpus, its approach is more global.
word2vec
There are two main approaches for word2vec, in which the algorithm loops through the worlds of the sentence. For each current word w it will try to predict
the neighboring words from w and its context, this is the Skip-Gram approach
w from its context, this is the CBOW approach
Hence, word2vec will produce a similar embedding for words with similar contexts, for instance a noun in singular and its plural, or two synonyms.
Glove
The main intuition underlying the Glove model is the simple observation that ratios of word-word co-occurrence probabilities have the potential for encoding some form of meaning. In other words the embeddings are based on the computation of distances between pairs of target words. The model computes the distance between two target words in a text by analyzing the co-occurence of those two target words with some other probe words (contextual words).
https://nlp.stanford.edu/projects/glove/
For example, consider the co-occurrence probabilities for target words "ice" and "steam" with various probe words from the vocabulary. Here are some actual probabilities from a 6 billion word corpus:
As one might expect, "ice" co-occurs more frequently with "solid" than it does with "gas", whereas "steam" co-occurs more frequently with "gas" than it does with "solid". Both words co-occur with their shared property "water" frequently, and both co-occur with the unrelated word "fashion" infrequently. Only in the ratio of probabilities does noise from non-discriminative words like "water" and "fashion" cancel out, so that large values (much greater than 1) correlate well with properties specific to "ice", and small values (much less than 1) correlate well with properties specific of "steam". In this way, the ratio of probabilities encodes some crude form of meaning associated with the abstract concept of thermodynamic phase.
Also, Glove is very good at analogy, and performs well on the word2vec dataset.