+jVddlmZddlmZddlmZeGddeZdS)) annotations) dataclass)BaseTrainingArgumentsceZdZdZdS)SparseEncoderTrainingArgumentsa SparseEncoderTrainingArguments extends :class:`~sentence_transformers.base.training_args.BaseTrainingArguments` with additional arguments specific to Sentence Transformers. See :class:`~transformers.TrainingArguments` for the complete list of available arguments. Args: output_dir (`str`): The output directory where the model checkpoints will be written. prompts (`Union[Dict[str, Dict[str, str]], Dict[str, str], str]`, *optional*): The prompts to use for each column in the training, evaluation and test datasets. Four formats are accepted: 1. `str`: A single prompt to use for all columns in the datasets, regardless of whether the training/evaluation/test datasets are :class:`datasets.Dataset` or a :class:`datasets.DatasetDict`. 2. `Dict[str, str]`: A dictionary mapping column names to prompts, regardless of whether the training/evaluation/test datasets are :class:`datasets.Dataset` or a :class:`datasets.DatasetDict`. 3. `Dict[str, str]`: A dictionary mapping dataset names to prompts. This should only be used if your training/evaluation/test datasets are a :class:`datasets.DatasetDict` or a dictionary of :class:`datasets.Dataset`. 4. `Dict[str, Dict[str, str]]`: A dictionary mapping dataset names to dictionaries mapping column names to prompts. This should only be used if your training/evaluation/test datasets are a :class:`datasets.DatasetDict` or a dictionary of :class:`datasets.Dataset`. batch_sampler (Union[:class:`~sentence_transformers.base.sampler.BatchSamplers`, `str`, :class:`~sentence_transformers.base.sampler.DefaultBatchSampler`, Callable[[...], :class:`~sentence_transformers.base.sampler.DefaultBatchSampler`]], *optional*): The batch sampler to use. See :class:`~sentence_transformers.base.sampler.BatchSamplers` for valid options. Defaults to ``BatchSamplers.BATCH_SAMPLER``. multi_dataset_batch_sampler (Union[:class:`~sentence_transformers.base.sampler.MultiDatasetBatchSamplers`, `str`, :class:`~sentence_transformers.base.sampler.MultiDatasetDefaultBatchSampler`, Callable[[...], :class:`~sentence_transformers.base.sampler.MultiDatasetDefaultBatchSampler`]], *optional*): The multi-dataset batch sampler to use. See :class:`~sentence_transformers.base.sampler.MultiDatasetBatchSamplers` for valid options. Defaults to ``MultiDatasetBatchSamplers.PROPORTIONAL``. router_mapping (`Dict[str, str] | Dict[str, Dict[str, str]]`, *optional*): A mapping of dataset column names to Router routes, like "query" or "document". This is used to specify which Router submodule to use for each dataset. Two formats are accepted: 1. `Dict[str, str]`: A mapping of column names to routes. 2. `Dict[str, Dict[str, str]]`: A mapping of dataset names to a mapping of column names to routes for multi-dataset training/evaluation. learning_rate_mapping (`Dict[str, float] | None`, *optional*): A mapping of parameter name regular expressions to learning rates. This allows you to set different learning rates for different parts of the model, e.g., `{'SparseStaticEmbedding\.*': 1e-3}` for the SparseStaticEmbedding module. This is useful when you want to fine-tune specific parts of the model with different learning rates. N)__name__ __module__ __qualname____doc__`C:\Projects\DSCons\.venv\Lib\site-packages\sentence_transformers\sparse_encoder\training_args.pyrrs''''r rN) __future__r dataclassesr(sentence_transformers.base.training_argsrrr r rrs|""""""!!!!!!JJJJJJ (((((%:(( (((r