Metadata-Version: 2.1
Name: doccano
Version: 1.5.5
Summary: doccano, text annotation tool for machine learning practitioners
Home-page: https://github.com/doccano/doccano
Author: Hironsan
Author-email: hiroki.nakayama.py@gmail.com
License: MIT
Description: 
        <div align="center">
          <img src="https://raw.githubusercontent.com/doccano/doccano/master/docs/images/logo/doccano.png">
        </div>
        
        # doccano
        
        [![Codacy Badge](https://app.codacy.com/project/badge/Grade/35ac8625a2bc4eddbff23dbc61bc6abb)](https://www.codacy.com/gh/doccano/doccano/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=doccano/doccano&amp;utm_campaign=Badge_Grade)
        [![doccano CI](https://github.com/doccano/doccano/actions/workflows/ci.yml/badge.svg)](https://github.com/doccano/doccano/actions/workflows/ci.yml)
        
        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](http://doccano.herokuapp.com).
        
        ![Demo image](https://raw.githubusercontent.com/doccano/doccano/master/docs/images/demo/demo.gif)
        
        ## 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 the following dependencies:
        
        - [Git](https://git-scm.com)
        - [Docker](https://www.docker.com)
        - [Docker Compose](https://docs.docker.com/compose)
        
        ### pip installation
        
        To install doccano, simply run:
        
        ```bash
        pip install doccano
        ```
        
        After installation, run the following commands:
        
        ```bash
        # Initialize database.
        doccano init
        # Create a super user.
        doccano createuser --username admin --password pass
        # Start a web server.
        doccano webserver --port 8000
        ```
        
        In another terminal, run the following command:
        
        ```bash
        # Start the task queue to handle file upload/download.
        doccano task
        ```
        
        Go to <http://127.0.0.1:8000/>.
        
        By default, sqlite3 is used for the default database. If you want to use PostgreSQL, install the additional dependency:
        
        ```bash
        pip install 'doccano[postgresql]'
        ```
        
        Create an .env file with variables in the following format, each on a new line:
        
        ```bash
        POSTGRES_USER=doccano
        POSTGRES_PASSWORD=doccano
        POSTGRES_DB=doccano
        ```
        
        Then, pass it to docker run with the --env-file flag:
        
        ```bash
        docker run --rm -d \
            -p 5432:5432 \
            -v postgres-data:/var/lib/postgresql/data \
            --env-file .env \
            postgres:13.3-alpine
        ```
        
        And set `DATABASE_URL` environment variable:
        
        ```bash
        # Please replace each variable.
        DATABASE_URL=postgres://${POSTGRES_USER}:${POSTGRES_PASSWORD}@localhost:5432/${POSTGRES_DB}?sslmode=disable
        ```
        
        Now run the command as before:
        
        ```bash
        doccano init
        doccano createuser --username admin --password pass
        doccano webserver --port 8000
        
        # In another terminal.
        # Don't forget to set DATABASE_URL
        doccano task
        ```
        
        ### 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=admin@example.com" \
          -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 <http://127.0.0.1:8000/>.
        
        ### Docker Compose
        
        You need to clone the repository:
        
        ```bash
        git clone https://github.com/doccano/doccano.git
        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 https://github.com/doccano/doccano.git --config core.autocrlf=input
        ```
        
        Then, create an `.env` file with variables in the following format(see [./config/.env.example](https://github.com/doccano/doccano/blob/master/config/.env.example)):
        
        ```plain
        # platform settings
        ADMIN_USERNAME=admin
        ADMIN_PASSWORD=password
        ADMIN_EMAIL=admin@example.com
        
        # rabbit mq settings
        RABBITMQ_DEFAULT_USER=doccano
        RABBITMQ_DEFAULT_PASS=doccano
        
        # database settings
        POSTGRES_USER=doccano
        POSTGRES_PASSWORD=doccano
        POSTGRES_DB=doccano
        ```
        
        #### Production
        
        After running the following command, access <http://0.0.0.0/>.
        
        ```bash
        docker-compose -f docker-compose.prod.yml --env-file ./config/.env.example up
        ```
        
        #### Development
        
        After running the following command, access <http://127.0.0.1:3000/>. If you want to use the admin site, please access <http://127.0.0.1:8000/admin/>.
        
        ```bash
        docker-compose -f docker-compose.dev.yml --env-file ./config/.env.example up
        ```
        
        You can run the the test codes for the backend with the following command:
        
        ```bash
        docker exec doccano_backend_1 python backend/manage.py test api
        ```
        
        ### One-click Deployment
        
        | Service | Button |
        |---------|---|
        | AWS[^1]   | [![AWS CloudFormation Launch Stack SVG Button](https://cdn.rawgit.com/buildkite/cloudformation-launch-stack-button-svg/master/launch-stack.svg)](https://console.aws.amazon.com/cloudformation/home?#/stacks/new?stackName=doccano&templateURL=https://doccano.s3.amazonaws.com/public/cloudformation/template.aws.yaml)  |
        | Heroku  | [![Deploy](https://www.herokucdn.com/deploy/button.svg)](https://dashboard.heroku.com/new?template=https%3A%2F%2Fgithub.com%2Fdoccano%2Fdoccano)  |
        <!-- | GCP[^2] | [![GCP Cloud Run PNG Button](https://storage.googleapis.com/gweb-cloudblog-publish/images/run_on_google_cloud.max-300x300.png)](https://console.cloud.google.com/cloudshell/editor?shellonly=true&cloudshell_image=gcr.io/cloudrun/button&cloudshell_git_repo=https://github.com/doccano/doccano.git&cloudshell_git_branch=CloudRunButton)  | -->
        
        > [^1]: (1) EC2 KeyPair cannot be created automatically, so make sure you have an existing EC2 KeyPair in one region. Or [create one yourself](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html#having-ec2-create-your-key-pair). (2) If you want to access doccano via HTTPS in AWS, here is an [instruction](https://github.com/doccano/doccano/wiki/HTTPS-setting-for-doccano-in-AWS).
        <!-- > [^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](https://cloud.google.com/run/docs/concepts). -->
        
        ## FAQ
        
        - [How to create a user](https://doccano.github.io/doccano/faq/#how-to-create-a-user)
        - [How to add a user to your project](https://doccano.github.io/doccano/faq/#how-to-add-a-user-to-your-project)
        - [How to change the password](https://doccano.github.io/doccano/faq/#how-to-change-the-password)
        
        See the [documentation](https://doccano.github.io/doccano/) for details.
        
        ## 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](https://github.com/doccano/doccano/wiki/How-to-Contribute-to-Doccano-Project)
        
        ## Citation
        
        ```tex
        @misc{doccano,
          title={{doccano}: Text Annotation Tool for Human},
          url={https://github.com/doccano/doccano},
          note={Software available from https://github.com/doccano/doccano},
          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](https://github.com/Hironsan).
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Description-Content-Type: text/markdown
Provides-Extra: postgresql
Provides-Extra: mssql
