ó >:jÝ ãóH•SSKJr SSKJr SSKJr \"SS\55rg)é)Ú annotations)Ú dataclass)ÚBaseTrainingArgumentscó•\rSrSrSrSrg)ÚCrossEncoderTrainingArgumentséaS CrossEncoderTrainingArguments 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, str], str]`, *optional*): The prompts to use in the training, evaluation and test datasets. Because CrossEncoder inputs from multiple columns are combined into pairs, per-column prompts are not supported. Two formats are accepted: 1. `str`: A single prompt to use for all 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 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`. batch_sampler (Union[:class:`~sentence_transformers.sentence_transformer.training_args.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.sentence_transformer.training_args.BatchSamplers` for valid options. Defaults to ``BatchSamplers.BATCH_SAMPLER``. multi_dataset_batch_sampler (Union[:class:`~sentence_transformers.sentence_transformer.training_args.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.sentence_transformer.training_args.MultiDatasetBatchSamplers` for valid options. Defaults to ``MultiDatasetBatchSamplers.PROPORTIONAL``. router_mapping (`Dict[str, str]`, *optional*): A mapping of dataset names to Router routes, like "slow", "fast". Because CrossEncoder inputs from multiple columns are combined into pairs, per-column router mappings are not supported. Only a per-dataset mapping is accepted, e.g. ``{'dataset_a': 'slow', 'dataset_b': 'fast'}``. 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)Ú__name__Ú __module__Ú __qualname__Ú__firstlineno__Ú__doc__Ú__static_attributes__r óÚn/home/mande/repo/quber/.venv/lib/python3.13/site-packages/sentence_transformers/cross_encoder/training_args.pyrrs†ô!rrN)Ú __future__rÚ dataclassesrÚ(sentence_transformers.base.training_argsrrr rrÚrs*ðÝ"å!åJð ô"Ð$9ó"ó ñ"r