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I'm using an LSTM to achieve a classification problem. I have a dataset composed by sentences, each sentence is composed by a variable number of words and I have to predict a label for each word of each sentence.

For example, I have a dataset of shape (300000, 800), so 300000 words and each word is made of 800 features (word embeddings, etc...). Moreover, each sentence is divided in subtrees, which each subtree is a syntactic dependency tree between words. My task is to learn the dependencies between words of a subtree.

I thought to train the network using timesteps=1 and the train_on_batch function, where the batch size is variable and equal to the subtree cardinality.

Is it a reasonable process?

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