Product labelling
Application for labelling transaction receipts, developed as part of the transaction data project. Predictions are made using the fastText algorithm.
The application available at ~/home includes two parts :
- The main part aims at simplifying the labelling process. One label from a file are automatically offered to the user who has to choose the rigth associated category/label.
- The second one enables users to visualize the results (preprocessing and predictions of the model).
Quick start
Using docker
docker run --env inseefrlab/product-labelling
Configuration
Each variable can be overriden using environment variables.
Product-labelling configuration
Key | Default | Description |
---|---|---|
model |
none |
URL of text classification model - fasttext model saved with .ftz extension (must be configured) |
nomenclature |
none |
URL of a CSV file which contains complete list of nomenclature products with no header |
db_type |
sqlite3 |
Other supported mode : postgres |
Product-labelling configuration if dbtype
==postgres
Key | Default | Description |
---|---|---|
db_password |
none |
See django configuration (must be configured) |
db_name |
none |
See django configuration (must be configured) |
db_user |
none |
See django configuration (must be configured) |
db_host |
localhost |
See django configuration |
db_port |
5432 |
See django configuration |