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I find your question bit convoluted, so I will answer with the following bullet points: Train your own word embeddings: There are many implementations out there, gensim is one. Find related articles: On that point, without being an expert, I would suggest to do some research on Topic Modelling. There are also a lot of libraries you can use. Word embeddings ...


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So Named Entity Recognition is a mechanism where you ask your network to learn about how to detect entities given word vectors as the input. The theoretical aspect of word embeddings is that based on your construction of sentences, the word embeddings for Orange and Apple are very similar i.e their cosine angle is very small. In Named entity recognition you ...


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So, from what I understand from the question, you want to get an idea of how word2vec works so you can assess how well the resulting context vectors from this model will help discriminate between words by their meaning. Word2vec works on the premise of the distributional hypothesis (https://en.m.wikipedia.org/wiki/Distributional_semantics#:~:text=The%...


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So, after a lot of digging, I found something in the comment section. They are document embeddings. There is a github repo that specifies an API. Paper on arxiv Example usage of a similar approach Relevant Comments from the Kaggle Comment section on the Data Update Log for the CORD19 Dataset: Comment 1 Comment 2


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Generally, combining word vectors in a single sentence / document representation does not work extremely well, although the average embedding has been used in fastText and pooling in this paper. You can also use autoencoders to try and predict the word distribution, similar to a bag-of-words approach, like here.


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I don't think it makes a lot of sense to compare a generic TPU to a generic GPU. There is probably a factor of 10 or greater between a low-end GPU and the best GPUs on the market in terms of compute capability. Google has itself developed 3 generations of TPUs, each more powerful than the last. I haven't kept up with the latest developments, but last I was ...


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inputs = Input(shape=[1]) emb = Embedding(input_dim=cap_shape_dummy.shape[1], output_dim=3)(inputs) x = Flatten()(emb) output = Dense(units=1,activation='sigmoid')(x) model = Model(inputs=inputs,outputs=output)


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Surely, I will good idea to write embedding on some database like SQLite,Postgres or RocksDB. I highly appreciate using RocksDB as it is store data in the form of a key-value pair which is the best suit for your use case. Maybe it will take little more time but your main issue will get resolve through it. If u directly deal with python than there is support ...


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