Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly train on a text using a pretrained model.
- A modern neural network architecture which utilizes new techniques as attention-weighting and skip-embedding to accelerate training and improve model quality.
- Able to train on and generate text at either the character-level or word-level.
- Able to configure RNN size, the number of RNN layers, and whether to use bidirectional RNNs.
- Able to train on any generic input text file, including large files.
- Able to train models on a GPU and then use them with a CPU.
- Able to utilize a powerful CuDNN implementation of RNNs when trained on the GPU, which massively speeds up training time as opposed to normal LSTM implementations.
- Able to train the model using contextual labels, allowing it to learn faster and produce better results in some cases.
- Able to generate text interactively for customized stories.
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