Upload HTML predictions
How to upload predictions on HTML data in a model run and sample upload formats.
Supported annotations
To import annotations in Labelbox, you need to create the annotations payload. This section shows how to declare the payloads for each supported annotation type.
Classification: Radio (single-choice)
Classification: Nested radio
Classification: Nested checklist
Classification: Checklist (multi-choice)
Classification: Free-form text
Example: Upload predictions to model run
To upload predictions to a model run:
Before you start
You will need to import these libraries to use the code examples in this section:
Replace the value of API_KEY
with a valid API key to connect to the Labelbox client.
Step 1: Import data rows into Catalog
Step 2: Set up ontology
Your project should include an ontology that supports your annotations. To ensure feature schema matches, the tool names and classification names should match the name
field in your annotations.
Step 3: Create model and model run
Step 4: Send data rows to model run
Step 5: Create prediction payload
Use the examples in Supported annotations to create your annotation payloads; you can use declare them as Python dictionaries or NDJSON objects. Examples of each type are shown here; they also show how to compose annotations into labels attached to the data rows.
The resulting label_prediction
and label_prediction_ndjson
from each approach demonstrates each supported annotation type.
Step 6: Upload predictions payload to model run
Step 7: Send annotations to model run
(Optional) To send annotations to a model run:
- Import them into a project
- Create a label payload
- Send Send them to the model run