- Riverscript to process the input text to output text
- Text Generator, All about Chatbot
- Markov Chain
- Deep Learning
- TensorFlow’s sequence to sequence library
Neural Network Text
App Recommanded by Janelle Shanne:
1. textgen-rnn – an open-source framework by Max Woolf, written in Python and powered by Tensorflow. It’s the easiest to install (though you still have to know your way around command line a bit) and comes pre-trained so you can get interesting results even from tiny datasets. It runs fine on an ordinary computer’s CPU, and lets you train the same network successively on different datasets, which is fun. It’s not as powerful as the other frameworks, but just fine for simple lists of names. (tested 15th may )
2. tensorflow char-rnn – an open-source framework by Chen Liang, written in Python. It has tons of flexibility, including the ability to adjust dropout, save frequency, and number of saved snapshots during training, and the ability to adjust temperature during sampling. There’s a tutorial here for running it on AWS, and I’m hoping to find a good tutorial for Google Cloud as well.
3. Andrej Karpathy’s char-rnn, an open-source neural network framework for torch (written in Lua). This one has great flexibility in training/sampling parameters, and it seems to run faster on my 2010 Macbook Pro’s CPU than the python/tensorflow models. I’ve been using this one for the simpler datasets.
- prepend the seed with a pre-seed (another paragraph of text) to push the LSTM into a desired state.
- Use high quality sample of output from the model you’re seeding with length approximately equal to the sequence length (see above) you set during training.
- Seed the LSTM with a meaningful text, that the machine would complete
- build a data set , corpus
- Choosing the right settings for a given corpus
- train the model
- generate output
- train again