vira-dialog-act-classification

Scope

The purpose of this project is to train and serve a language model of dialog-act classification for the VIRA chatbot.

Usage

This repo should be used in the following scenarios, in this order:

Adding new dialog-acts

Adding new dialog-acts is normally done on a maintainer personal computer using a CSV editor.

If the repo does not exist on your computer, clone it using the command:

git clone https://github.com/IBM/vira-dialog-act-classification.git

To add new dialog-acts:

1. Make sure you are using the latest version of the repo using the command git pull
2. Edit the CSV files under dialog-act_dataset using a CSV editor.
3. Commit your changes to the repo. It is recommended to create a Pull Request when making changes, as described under [Maintenance].

Packaging the repository for deployment

This step is required only when there are changes to the Python code. It can be executed from the computer used for adding new dialog-acts.

Pre-requisites:

1. If the repo does not exist on your computer, clone it as shown in the previous section.
2. Make sure that Docker Desktop is installed on your computer
3. Create a repository on the Docker hub as explained here

To package the repo for deployment:

1. Run: docker build . -t vira-dialog-act-classifier TODO
2. Run: docker push <hub-user>/<repo-name>:vira-dialog-act-classifier

Training a new dialog-act classification model

In many cases, training a new model can be done on the same computer that was used for adding new dialog-acts. However, it is also possible to use a separate computer, preferably one with a GPU, for faster execution.

If this repo does not exist on the computer used for training, clone it using the command:

git clone https://github.com/IBM/vira-dialog-act-classification.git

And in addition:

1. Make sure you have Python 3.7+ installed.
2. Open a shell and change directory to the repo root directory
3. Create a new Python virtual environment: python -m venv venv
4. Activate the virtual environment using: source venv/bin/activate
5. Install the dependencies using: pip install -r requirements.txt
6. Deactivate the virtual environment by running: deactivate
7. Register at 🤗 Hugging Face and obtain your authentication token from the tokens page

To train a new model:

1. Make sure you are using the latest version of the repo using the command git pull
2. Activate the virtual environment using: source venv/bin/activate
3. Run the trainer script python trainer.py and wait until it finishes
4. Upload the new model and the dataset to HuggingFace hub using the command: python upload.py <your_auth_token>.

Deploying a dialog-act classification model

Deployment is normally done on a remote server that is publicly available on the web and supports containerized services such as Kubernetes. However, for testing purposes it is possible to deploy on a personal computer. It is recommended, but not mandatory, to use a hardware with GPU.

To deploy the model on a remote server:

1. Configure the platform used for containerized services to run the docker image <hub-user>/<repo-name>:vira-dialog-act-classifier
2. Verify that the service is running by opening a browser at the URL https://<server-ip>/health

To deploy the model on a personal computer:

1. Make sure you have Docker Desktop installed
2. Run: docker run -p 8000:8000 <hub-user>/<repo-name>:vira-dialog-act-classifier
3. Verify that the service is running by opening a browser at the URL https://127.0.0.1:8000/health

Maintenance

Pull requests are very welcome! Make sure your patches are well tested. Ideally create a topic branch for every separate change you make. For example:

1. Create your feature branch (git checkout -b my-new-feature)
2. Commit your changes (git commit -am 'Added some feature')
3. Push to the branch (git push origin my-new-feature)
4. Create new Pull Request and ask another person to review and merge

License

All source files must include a Copyright and License header. The SPDX license header is preferred because it can be easily scanned.

If you would like to see the detailed LICENSE click here.

#
# Copyright 2020- IBM Inc. All rights reserved
# SPDX-License-Identifier: Apache2.0
#

More Information

More information can be found in these files:

• LICENSE
• CONTRIBUTING.md
• MAINTAINERS.md
• CHANGELOG.md
• dco.yml - This enables DCO bot for you, please take a look https://github.com/probot/dco for more details.

Notes

If you have any questions or issues you can create a new issue here.