chatbot-rnn

A toy chatbot powered by deep learning and trained on data from Reddit. Built on TensorFlow v1.4.0 and Python v3.5.1.

Here is a sample chat transcript (not cherry-picked). Lines that start with ">" are me; the lines without ">" are the chatbot's responses.

$ python3 chatbot.py
Creating model...
Restoring weights...

> Hi, how are you?
 I'm not the only one here that's having fun with you, it's all me.
> Who else is with you?
 Haha. I'm not the one who made the comment. I'm the one who said that and didn't think that much of anything. 
> Oh, I see. So what's new?
 Nothing. I just thought it was a bit of an overreaction. It's been a while since I've read it. 
> It's all good
 It's all good. It's one of those things that makes me smile :)
> That's great. This is actually my demo chat, so it's nice that you're keeping things positive!
 Thank you :) 
> You're welcome! It's a nice change from the way that you sometimes are...
 *blushes* Sorry, I should have clarified that I wasn't clear. I apologize for my ignorance.
> Sooo, where are you from?             
 I am from South America. Where you from?
> California. Do you like it in South America?
 I live in Australia, but south of Florida is one of the reasons I live in Florida. I've never been there, but I've never been there. 
> I see, a true citizen of the world
 Nope, I've never heard of it. I just spent a lot of time there. 
> Spend a lot of time where?
 I've never been there before, but I've heard it's about an hour and a half away. Maybe there's some sort of party called "Southern" but that's about it. 
> Maybe there is, that's a great observation.
 Not to mention it's all about being from another country.
> All right, guess I should wrap this up, nice talking with you
 I'm not going to argue with you. You're doing it wrong.
> Some might call that arguing. Any final words?
 Don't do that. I don't need to. 

Get Started

Run my pre-trained model

• Download my pre-trained model (2.3 GB). The zip file extracts into a folder named "reddit". Place that folder into the "models" directory of this project.

• Run the chatbot. Open a terminal session and run python3 chatbot.py. Warning: this pre-trained model was trained on a diverse set of frequently off-color Reddit comments. It can (and eventually will) say things that are offensive, disturbing, bizarre or sexually explicit. It may insult minorities, it may call you names, it may accuse you of being a pedophile, it may try to seduce you. Please don't use the chatbot if these possibilities would distress you!

Try playing around with the arguments to chatbot.py to obtain better samples:

• beam_width: By default, chatbot.py will use beam search with a beam width of 2 to sample responses. Set this higher for more careful, more conservative (and slower) responses, or set it to 1 to disable beam search.

• relevance: Two models are run in parallel: the primary model and the mask model. The mask model is scaled by the relevance value, and then the probabilities of the primary model are combined according to equation 9 in Li, Jiwei, et al. "A diversity-promoting objective function for neural conversation models." arXiv preprint arXiv:1510.03055 (2015). The state of the mask model is reset upon each newline character. The net effect is that the model is encouraged to choose a line of dialogue that is most relevant to the prior line of dialogue, even if a more generic response (e.g. "I don't know anything about that") may be more absolutely probable. Higher relevance values put more pressure on the model to produce relevant responses, at the cost of the coherence of the responses. Going much above 0.4 compromises the quality of the responses. Setting it to a negative value disables relevance, and this is the default, because I'm not confident that it qualitatively improves the outputs and it halves the speed of sampling.

These values can also be manipulated during a chat, and the model state can be reset, without restarting the chatbot:

$ python3 chatbot.py
Creating model...
Restoring weights...

> --temperature 1.3
[Temperature set to 1.3]

> --relevance 0.3
[Relevance set to 0.3]

> --relevance -1
[Relevance disabled]

> --topn 2
[Top-n filtering set to 2]

> --topn -1
[Top-n filtering disabled]

> --beam_width 5
[Beam width set to 5]

> --reset
[Model state reset]

Get training data

If you'd like to train your own model, you'll need training data. There are a few options here.

• Use pre-formatted Reddit training data. This is what the pre-trained model was trained on.

  Download the training data (2.1 GB). Unzip the monolithic zip file. You'll be left with a folder named "reddit" containing 34 files named "output 1.bz2", "output 2.bz2" etc. Do not extract those individual bzip2 files. Instead, place the whole "reddit" folder that contains those files inside the data folder of the repo. The first time you run train.py on this data, it will convert the raw data into numpy tensors, compress them and save them back to disk, which will create files named "data0.npz" through "data34.npz" (as well as a "sizes.pkl" file and a "vocab.pkl" file). This will fill another ~5 GB of disk space, and will take about half an hour to finish.

• Generate your own Reddit training data. If you would like to generate training data from raw Reddit archives, download a torrent of Reddit comments from the torrent links listed here. The comments are available in annual archives, and you can download any or all of them (~304 GB compressed in total). Do not extract the individual bzip2 (.bz2) files contained in these archives.

• Provide your own training data. Training data should be one or more newline-delimited text files. Each line of dialogue should begin with "> " and end with a newline. You'll need a lot of it. Several megabytes of uncompressed text is probably the minimum, and even that may not suffice if you want to train a large model. Text can be provided as raw .txt files or as bzip2-compressed (.bz2) files.

• Simulate the United States Supreme Court. I've included a corpus of United States Supreme Court oral argument transcripts (2.7 MB compressed) in the project under the data/scotus directory.

Once you have training data in hand (and located in a subdirectory of the data directory):

Thanks to Andrej Karpathy for his char-rnn repo, and to Sherjil Ozair for his TensorFlow port of char-rnn, which this repo is based on.