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I made textgenrnn (https://github.com/minimaxir/textgenrnn) as a higher-level approach to creating char-rnns, solving some issues such as the cold start problem (textgenrnn doesn't need a seed) and incorporating a few newer discoveries since 2015 such as Attention and CuDNN speedups.

There are other strategies for working with text to solve generic classification problems (e.g. BERT), but for text generation, LSTMs still can't be beat even though it still has issues with longer-term dependencies.




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