- 1.56.0 (latest)
- 1.55.0
- 1.54.1
- 1.53.0
- 1.52.0
- 1.51.0
- 1.50.0
- 1.49.0
- 1.48.0
- 1.47.0
- 1.46.0
- 1.45.0
- 1.44.0
- 1.43.0
- 1.39.0
- 1.38.1
- 1.37.0
- 1.36.4
- 1.35.0
- 1.34.0
- 1.33.1
- 1.32.0
- 1.31.1
- 1.30.1
- 1.29.0
- 1.28.1
- 1.27.1
- 1.26.1
- 1.25.0
- 1.24.1
- 1.23.0
- 1.22.1
- 1.21.0
- 1.20.0
- 1.19.1
- 1.18.3
- 1.17.1
- 1.16.1
- 1.15.1
- 1.14.0
- 1.13.1
- 1.12.1
- 1.11.0
- 1.10.0
- 1.9.0
- 1.8.1
- 1.7.1
- 1.6.2
- 1.5.0
- 1.4.3
- 1.3.0
- 1.2.0
- 1.1.1
- 1.0.1
- 0.9.0
- 0.8.0
- 0.7.1
- 0.6.0
- 0.5.1
- 0.4.0
- 0.3.1
ActiveLearningConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)Paramaters that configure active learning pipeline. Active learning will label the data incrementally by several iterations. For every iteration, it will select a batch of data based on the sampling strategy.
Attributes
| Name | Description |
| max_data_item_count |
int
Max number of human labeled DataItems. |
| max_data_item_percentage |
int
Max percent of total DataItems for human labeling. |
| sample_config |
`.data_labeling_job.SampleConfig`
Active learning data sampling config. For every active learning labeling iteration, it will select a batch of data based on the sampling strategy. |
| training_config |
`.data_labeling_job.TrainingConfig`
CMLE training config. For every active learning labeling iteration, system will train a machine learning model on CMLE. The trained model will be used by data sampling algorithm to select DataItems. |