Conversation AI Chatbot with a focus on keeping a consistent set of personality facts throughout conversation. Models trained include:
- Sequence to Sequence
- Transformer
- Multiple Encoders
This project consists of a Flask Website and Command Line Interface.
Watch the demo: https://www.youtube.com/watch?v=1Prv4J6LOuM&feature=youtu.be
To install the Command Line Interface, first install python3 and then use the following steps:
git clone https://github.com/psyfb2/Chatbot.git
cd Chatbot/chatbot
pip install -r requirements.txt
GloVe embedding file is not included in this github repository because of it's large size. Download GloVe.6B.zip and extract glove.6B.300d.txt into /chatbot/data folder.
The CLI is responsible for training, evaluating and interacting with the models.
Model choices include:
- seq2seq
- deep_seq2seq
- multiple_encoders
- deep_multiple_encoders
- transformer
One of these should be passed to either the train, eval or talk arguments.
| Argument Name | Description | Default Value |
|---|---|---|
| train | Name of the model to train | None |
| batch_size | Training batch size | 64 |
| epochs | Max number of training epochs | 100 |
| early_stopping_patience | Number of epochs to run without best validation loss decreasing | 7 |
| segment_embedding | Use segment embedding? | True |
| perform_pretraining | pretrain models on Movie and Daily Dialog datasets? | False |
| verbose | Display loss for each batch? | 0 |
| min_epochs | Number of epochs to run regardless of early stopping | 30 |
| glove_filename | Name of the glove file to use in the data folder | glove.6B.300d.txt |
| eval | Name of the model to evaluate using Perplexity and F1 | None |
| talk | Name of the model to interact with | None |
| beam_width | Beam width to use in beam search | 3 |
| beam_search | Use beam search? | True |
| plot_attention | Plot attention weights, requires beam_search to be false. | False |
The CLI is at the directory chatbot/models/main.py
Example usage to train Seq2Seq model:
python main.py --train seq2seq
Example usage to evaluate Multiple Encoders model:
python main.py --eval multiple_encoders
Example usage to interact with Transformer model:
python main.py --talk transformer
Note that trained models are not included in this github repo because of their large size.
Trained models can be found here, just put them in the chatbot/saved_models folder:
Trained Models
Evaluation results can be found at chatbot/models/results.txt
The models are deployed using a Flask backend.
The live version can be found here (currently down but will be up again soon): Live Version