doccano

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doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequence to sequence tasks. So, you can create labeled data for sentiment analysis, named entity recognition, text summarization and so on. Just create a project, upload data and start annotating. You can build a dataset in hours.

Demo

You can try the annotation demo.

!Demo image

Features

• Collaborative annotation
• Multi-language support
• Mobile support
• Emoji :smile: support
• Dark theme
• RESTful API

Usage

Three options to run doccano:

• pip(experimental)
• Docker
• Docker Compose
  • production
  • development

For docker and docker compose, you need to install dependencies:

• Git
• Docker
• Docker Compose

pip installation

To install doccano, simply run:

bash
pip install doccano

After installation, simply run the following command:

bash
# Initialize database.
doccano init
# Create a super user.
doccano createuser --username admin --password pass
# Start the webserver.
doccano webserver --port 8000

And in another terminal, run the following command:

bash
# Start the task queue.
doccano task

Go to <[***]>.

Docker

As a one-time setup, create a Docker container as follows:

bash
docker pull doccano/doccano
docker container create --name doccano \
  -e "ADMIN_USERNAME=admin" \
  -e "ADMIN_EMAIL=***" \
  -e "ADMIN_PASSWORD=password" \
  -p 8000:8000 doccano/doccano

Next, start doccano by running the container:

bash
docker container start doccano

To stop the container, run docker container stop doccano -t 5. All data created in the container will persist across restarts.

Go to <[***]>.

Docker Compose

You need to clone the repository:

bash
git clone [***]
cd doccano

Note for Windows developers: Be sure to configure git to correctly handle line endings or you may encounter status code 127 errors while running the services in future steps. Running with the git config options below will ensure your git directory correctly handles line endings.

bash
git clone [***] --config core.autocrlf=input

Production

Set the superuser account credentials in the docker-compose.prod.yml file:

yml
ADMIN_USERNAME: "admin"
ADMIN_PASSWORD: "password"

If you use Google Analytics, set the tracking:

yml
GOOGLE_TRACKING_ID: "UA-***-1"

Run doccano:

bash
$ docker-compose -f docker-compose.prod.yml up

Go to <[***]>.

Development

Set the superuser account credentials in the docker-compose.dev.yml file:

yml
ADMIN_USERNAME: "admin"
ADMIN_PASSWORD: "password"

Run Doccano:

bash
$ docker-compose -f docker-compose.dev.yml up

Go to <[***]>.

Run Backend API-Tests:

You can run the API-Tests for the backend with the following command:

bash
docker exec doccano_backend_1 pipenv run app/manage.py test api

Add annotators (optionally)

If you want to add annotators/annotation approvers, see Frequently Asked Questions

One-click Deployment

Documentation

See here.

Contribution

As with any software, doccano is under continuous development. If you have requests for features, please file an issue describing your request. Also, if you want to see work towards a specific feature, feel free to contribute by working towards it. The standard procedure is to fork the repository, add a feature, fix a bug, then file a pull request that your changes are to be merged into the main repository and included in the next release.

Here are some tips might be helpful. How to Contribute to Doccano Project

Citation

tex
@misc{doccano,
  title={{doccano}: Text Annotation Tool for Human},
  url={[***]},
  note={Software available from [***]},
  author={
    Hiroki Nakayama and
    Takahiro Kubo and
    Junya Kamura and
    Yasufumi Taniguchi and
    Xu Liang},
  year={2018},
}

Contact

For help and feedback, please feel free to contact the author.

Footnotes

1. (1) EC2 KeyPair cannot be created automatically, so make sure you have an existing EC2 KeyPair in one region. Or create one yourself. (2) If you want to access doccano via HTTPS in AWS, here is an instruction. ↩

2. Although this is a very cheap option, it is only suitable for very small teams (up to 80 concurrent requests). Read more on Cloud Run docs. ↩