Easily train your own text-generating neural network of any size and complexity
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.