In this project I train and evaluate three different methods for next-word prediction using LSTMs and continuous value inputs and outputs. With what I call sequence to token (S2T), an input sequence is encoded as float values (embeddings) and used to predict a final masked token in the sequence.
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In this project I train and evaluate three different methods for next-word prediction using LSTMs and continuous value inputs and outputs. With what I call sequence to token (S2T), an input sequence is encoded as float values (embeddings) and used to predict a final masked token in the sequence.
eschaffn/Continuous-Representation-Experiment
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In this project I train and evaluate three different methods for next-word prediction using LSTMs and continuous value inputs and outputs. With what I call sequence to token (S2T), an input sequence is encoded as float values (embeddings) and used to predict a final masked token in the sequence.
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