<p><a href="https://arxiv.org/pdf/1411.2738.pdf" rel="nofollow noreferrer">This paper</a> has a great explanation of how Word2Vec operates both for CBOW and Skip-Gram.</p>
<p>The words are fed in simultaneously as one-hot encoded vectors. Then the one-hot representations are multiplied by the weight matrix (which doubles up as the word embedding matrix in the Word2Vec architecture). This multiplication boils down to extracting a single row from W - the row corresponding to the word embedding in question.</p>
<p>The hidden layer is then just an aggregation (average in most cases) of each of the word embeddings extracted using the one-hot encoded inputs.</p>
<p><a href="https://i.stack.imgur.com/Ubos3.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Ubos3.png" alt="Word2Vec CBOW Model" /></a></p>
doubts regarding word2vec Continuous bag-of-words (CBOW)
<p><a href="https://i.stack.imgur.com/7BXvM.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/7BXvM.png" alt="enter image description here"></a></p>

<p>For the CBOW model, the INPUT words are feed into the training model simultaneously or one by one?</p>

<p>Thanks</p>