Labelly is a self-hostable, file-based, local-first solution for labelling text data, e.g. for AI training purposes.
Through it's local first approach, it's especially suitable for sensitive data like medical records or legal documents.
The easiest way to get Labelly running is through docker:
Clone the GitHub repo and run docker build -t labelly .
Labelly expects a /data folder in the root path with the text files to label in data/files.
Alternatively, you can providefile called input.csv with the data to label. Labelly expects to have input.csv to have a text column at least.
An id column is optional and will be created with the row number as the id when missing.
An example of a proper data folder can be found in _example_data.
Additionally you have to your define your labels in a labels.json file in the data folder.
An example labels.json file can be found here:
{
"Examination": {
"options": [
"Normal",
"Abnormal",
"Unsure"
]
},
"Intervention": {
"type": "multiple",
"options": [
"None",
"Medication",
"Surgery",
"Other"
]
}
}Each key represents a label group named after the key. The type specifies if it's a multi-label or single-label label group or not (defaults to single-label label group). The options specify the different options the label can have.
The results will be written into a data/results.csv file.
docker run -p 8000:8000 -v $(pwd)/data:/app/data labelly
Swap out $(pwd)/data for a proper path to your data on your local machine. The folder wild be mounted into the correct location of the docker container.
This is a very early proof of concept. Current todos can be found in TODOS.md.
Do you have feedback?
Write me on Twitter or write me a mail: mariusvach [at] gmail.com