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Embedding vs Latent Space Due to Machine Learning's recent and rapid renaissance, and the fact that it draws from many distinct areas of mathematics, statistics, and computer science, it often has a number of different terms for the same or similar concepts. "Latent space" and "embedding" both refer to an (often lower-dimensional) ...

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If you have to move a lot of data around during training (like retrieving batches from disk/network/what have you), it's much faster to do so as a rank-3 tensor of [batches, documents, indices] than as a rank-4 tensor of [batches, documents, indices, vectors]. In this case, while the embedding is O(1) wherever you put it, it's more efficient to do so as part ...

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A language model aims to estimate the probability of one or more words given the surrounding words. Given a sentence composed of $w_{1},...,w_{i-1},\_ , w_{i+1},..,w_{n}$, you can find which is the i-th missing word using a language model. In this way, you can estimate which is the most probable word using for example the conditional probability \$P(w_i=w|w_1,...

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Simplified: Word Embeddings does not consider context, Language Models does. For e.g Word2Vec, GloVe, or fastText, there exists one fixed vector per word. Think of the following two sentences: The fish ate the cat. and The cat ate the fish. If you averaged their word embeddings, they would have the same vector, but, in reality, their meaning (semantic) ...

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I have found a good answer in this blog post The Transformer: Attention Is All You Need: we learn a “word embedding” which is a smaller real-valued vector representation of the word that carries some information about the word. We can do this using nn.Embedding in Pytorch, or, more generally speaking, by multiplying our one-hot vector with a learned weight ...

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No, neither Word2Vec nor GloVe is used as Transformers are a newer class of algorithms. Word2Vec and GloVe are based on static word embeddings while Transformers are based on dynamic word embeddings. The embeddings are trained from scratch.

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I finally grasped the concept of word embedding. Thanks to @nbro, after reading the 2 articles s/he recommended What Are Word Embeddings for Text? and Word embeddings the 1st article gives me a good idea about the big picture of the Word Embeddings; whereas the 2nd article is actually the one which clears my mind. I am an visual person, I understand ...

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The specific term you are looking for is "word embedding" and not just "embedding". How to numerically represent textual data? Neural networks (typically) require as inputs (and produce as outputs) numerical data (i.e. numbers, vectors, matrices, or higher-dimensional arrays). So, when processing textual data, we first need to encode (or ...

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There are multiple ways to get word embedding from a corpus. Count Vectorizer: You can use the CountVectorizer() from sklearn.feature_extraction.text and then use the fit_transform() if the corpus has been converted into a list of sentences TF-IDF Vectorizer: You can use the TfidfVectorizer from sklearn.feature_extraction.text and then again use the ...

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It doesn't drops rows or columns, it acts directly on scalars. The Dropout Layer keras documentation explains it and illustrates it with an example : The Dropout layer randomly sets input units to 0 with a frequency of rate After an Dense Layer, the Dropout inputs are directly the outputs of the Dense layer neurons, as you said. After your embedding layer, ...

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I think you should use Keras embedding layer. It will be too easier than what you are doing. Steps Create Embedding Matrix add matrix to embedding layer while building model. You will find detailed article https://www.cs.uaf.edu/2011/spring/cs641/lecture/04_05_modeling.html

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Briefly: I think what you are looking for is an RNN network (Either LSTM or GRU) with the many-to-many topology. Explanation: Clearly your input is the sentences (or to be more precise, the an embedding of your sentences, because you cannot feed the raw text to the network). then for each sentence you want to assign a value, which means for n inputs, you ...

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