nemo_microservices.types.customization.dataset_parameters_param#
Module Contents#
Classes#
API#
- class nemo_microservices.types.customization.dataset_parameters_param.DatasetParametersParam#
Bases:
typing_extensions.TypedDict- in_batch_negatives: bool#
None
In-batch negatives treats every other example in a training batch as a negative sample during contrastive learning. When enabled, the model learns to distinguish the correct positive pair not just from explicitly provided hard negatives, but from all other examples in the same batch. This can improve training without adding extra labeled negative data.
- negative_sample_strategy: str#
None
How to select negatives when more are available than needed - Embedding Only. ‘first’ picks the first N; ‘random’ samples N negatives randomly.
- num_hard_negatives: int#
None
Number of negative documents to include per query for contrastive training.
Embedding Only
- tools: Iterable[nemo_microservices.types.customization.tool_schema_param.ToolSchemaParam]#
None
A list of tools that are available for training with tool calling