Upload text predictions
How to upload predictions on text data in a model run and sample upload formats.
Supported prediction types
To upload predictions in Labelbox, you need to create a predictions payload. This section shows how to declare payloads for each supported prediction type. You can declare payloads using Python annotation types (preferred) or as NDJSON objects.
Confidence scores are optional. If you do not include confidence scores in your prediction payloads, the prediction is treated as if it had a confidence value of one (1
).
Entity
Classification: radio (single choice)
Classification: radio nested
Classification: checklist nested
Classification: checklist (multiple choice)
Classification: free-form text
Example: Upload predictions to model run
To upload predictions to a model run:
Before you start
These examples require the following libraries:
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 for predictions
Your model run ontology should support all tools and classifications required by your predictions.
this example shows how to create an ontology containing all supported prediction types.
Step 3: Create model and model run
Step 4: Send data rows to model run
Step 5: Create prediction payloads
See supported prediction types for help creating prediction payloads. You can declare predictions as Python annotation types (preferred) or NDJSON objects. These examples show each type and describe how to compose predictions into labels attached to the data rows.
The resulting label_ndjson_predictions
and label_predictions
payloads should have exactly the same prediction content (except for the uuid
string values).
Step 6: Upload payload to model run
Step 7: Send annotations to model run
This step is optional.