Annotator is a fork of doccano project.
The first demo is a sequence labeling task: named-entity recognition. You just select text spans and annotate them. Doccano supports shortcut keys, so you can quickly annotate text spans.
The second demo is a text classification task: sentiment analysis. Since there may be more than one category, you can annotate with multiple labels.
The final demo is a sequence to sequence task: machine translation. Since there may be more than one response in sequence to sequence tasks, you can create multiple responses.
- Collaborative annotation
- Multi-Language support
- Emoji 😄 support
- (future) Auto labeling
- Python 3.6+
- Django 2.1.7+
- Node.js 8.0+
First of all, you have to clone the repository:
git clone https://github.com/hhuslamlab/annotator.git
cd annotator
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.
git clone https://github.com/hhuslamlab/annotator.git --config core.autocrlf=input
First we need to install the dependencies. Run the following commands:
sudo apt-get install libpq-dev unixodbc unixodbc-dev npm
pip install -r requirements.txt
cd app
Next we need to start the webpack server so that the frontend gets compiled continuously. Run the following commands in a new shell:
cd server/static
npm install
npm run build
# npm start # for developers
cd ..
Let’s start the development server and explore it.
Before running, we need to make migration. Run the following command:
python manage.py migrate
Next we need to create a user who can login to the admin site. Run the following command:
python manage.py create_admin --noinput --username "admin" --email "admin@example.com" --password "password"
Create the admin, annotator, and annotation approver roles to assign to users. Run the following command:
python manage.py create_roles
Developers can also validate that the project works as expected by running the tests:
python manage.py test server.tests
Finally, to start the server, run the following command:
python manage.py runserver
Optionally, you can change the bind ip and port using the command
python manage.py runserver <ip>:<port>
Open a Web browser and go to http://127.0.0.1:8000/login/. You should see the login screen:
Now, try logging in with the superuser account you created in the previous step. You should see the annotator project list page:
There is no project created yet. To create your project, make sure you’re in the project list page and select Create Project button. You should see the following screen:
In this step, you can select three project types: text classification, sequence labeling and sequence to sequence. You should select a type with your purpose.
After creating a project, you will see the "Import Data" page, or click Import Data button in the navigation bar. You should see the following screen:
You can upload the following types of files (depending on project type):
Text file: file must contain one sentence/document per line separated by new lines.CSV file: file must contain a header with"text"as the first column or be one-column csv file. If using labels the second column must be the labels.Excel file: file must contain a header with"text"as the first column or be one-column excel file. If using labels the second column must be the labels. Supports multiple sheets as long as format is the same.JSON file: each line contains a JSON object with atextkey. JSON format supports line breaks rendering.
Notice: Doccano won't render line breaks in annotation page for sequence labeling task due to the indent problem, but the exported JSON file still contains line breaks.
example.txt/csv/xlsx
EU rejects German call to boycott British lamb.
President Obama is speaking at the White House.
He lives in Newark, Ohio.
...
example.json
{"text": "EU rejects German call to boycott British lamb."}
{"text": "President Obama is speaking at the White House."}
{"text": "He lives in Newark, Ohio."}
...
Any other columns (for csv/excel) or keys (for json) are preserved and will be exported in the metadata column or key as is.
Once you select a TXT/JSON file on your computer, click Upload dataset button. After uploading the dataset file, we will see the Dataset page (or click Dataset button list in the left bar). This page displays all the documents we uploaded in one project.
Click Labels button in left bar to define your own labels. You should see the label editor page. In label editor page, you can create labels by specifying label text, shortcut key, background color and text color.
Click Users button in left bar to assign project users to annotator, admin, or annotation approval roles.
Now, you are ready to annotate the texts. Just click the Annotate Data button in the navigation bar, you can start to annotate the documents you uploaded.
After the annotation step, you can download the annotated data. Click the Edit data button in navigation bar, and then click Export Data. You should see below screen:
You can export data as CSV file or JSON file by clicking the button. As for the export file format, you can check it here: Export File Formats.
Each exported document will have metadata column or key, which will contain
additional columns or keys from the imported document. The primary use-case for metadata is to allow you to match exported data with other system
by adding external_id to the imported file. For example:
Input file may look like this:
import.json
{"text": "EU rejects German call to boycott British lamb.", "meta": {"external_id": 1}}
and the exported file will look like this:
output.json
{"doc_id": 2023, "text": "EU rejects German call to boycott British lamb.", "labels": ["news"], "username": "root", "meta": {"external_id": 1}}