>:j4SSKJr SSKrSSKrSSKJr SSKJrJr SSK J r SSK J r SSKJr SSKJr SS KJr SS KJrJrJrJr \R4"\5r\"S S \55rg) ) annotationsN)Callable) dataclassfield)Union)parse)TrainingArguments) __version__) ParallelMode) BatchSamplersDefaultBatchSamplerMultiDatasetBatchSamplersMultiDatasetDefaultBatchSamplerc^\rSrSr%Sr/SQr\"SSS0S9rS\S '\"\ RSS 0S9r S \S '\"\ RSS 0S9rS\S'\"\SS0S9rS\S'\"\SS0S9rS\S'\"SSS0S9rS\S'U4SjrU4SjrSrU=r$)BaseTrainingArgumentsaj BaseTrainingArguments extends :class:`~transformers.TrainingArguments` 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.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] | 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. )accelerator_config fsdp_config deepspeedgradient_checkpointing_kwargslr_scheduler_kwargslearning_rate_mappingpromptsrouter_mappingNhelpzThe prompts to use for each column in the datasets. Either 1) a single string prompt, 2) a mapping of column names to prompts, 3) a mapping of dataset names to prompts, or 4) a mapping of dataset names to a mapping of column names to prompts.)defaultmetadataz;Union[str, None, dict[str, str], dict[str, dict[str, str]]]rzThe batch sampler to use.zRUnion[BatchSamplers, str, DefaultBatchSampler, Callable[..., DefaultBatchSampler]] batch_samplerz'The multi-dataset batch sampler to use.zvUnion[MultiDatasetBatchSamplers, str, MultiDatasetDefaultBatchSampler, Callable[..., MultiDatasetDefaultBatchSampler]]multi_dataset_batch_samplerzA mapping of dataset column names to Router routes, like "query" or "document". Either 1) a mapping of column names to routes or 2) a mapping of dataset names to a mapping of column names to routes for multi-dataset training/evaluation. )default_factoryrrzA 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.z"Union[str, None, dict[str, float]]rcB[[5[S5:aS$S$)N5.0.0) parse_versiontransformers_versione/home/mande/repo/quber/.venv/lib/python3.13/site-packages/sentence_transformers/base/training_args.pyBaseTrainingArguments.us 6J(K}]dOe(e nkn nr'zThis argument is deprecated and will be removed in the future. If you're on Transformers v5+, then you should use `warmup_steps` instead as it also works with float values.z float | None warmup_ratioc>[[5[S5:aKURb=URS:Xa-URUlSUl[R S5 Oa[ UR[5(aBSURs=:aS:a+O O(URS:XaURUlSUl[TU]%5 [ UR[5(a[UR5O URUl [ UR[5(a[UR5O URUl [ UR[5(a&[ R""UR5UlUR&b UR&O0Ul[ UR&[5(a&[ R""UR&5UlUR*b UR*O0Ul[ UR*[5(a&[ R""UR*5UlSUlS UlUR0[2R4:Xa'UR6S :wa[R S 5 ggUR0[2R8:Xa?UR:(d-UR6S :wa[R S 5 SUlggg![ R$a GNf=f![ R$a [)S5ef=f![ R$a [)S5ef=f) Nr"raThe `warmup_ratio` argument is deprecated in Transformers v5+, and will also be removed from Sentence Transformers once support for Transformers v4 is dropped. Since you're using Transformers v5+, please use `warmup_steps` (as a float) to specify the warmup ratio instead.r#g?zThe `learning_rate_mapping` argument must be a dictionary mapping parameter name regular expressions to learning rates. A stringified dictionary also works.zThe `router_mapping` argument must be a dictionary mapping dataset column names to Router routes, like 'query' or 'document'. A stringified dictionary also works.TFunusedzCurrently using DataParallel (DP) for multi-gpu training, while DistributedDataParallel (DDP) is recommended for faster training. See https://sbert.net/docs/sentence_transformer/training/distributed.html for more information.zWhen using DistributedDataParallel (DDP), it is recommended to set `dataloader_drop_last=True` to avoid hanging issues with an uneven last batch. Setting `dataloader_drop_last=True`.)r$r%r+ warmup_stepsloggerwarning isinstancefloatsuper __post_init__rstrr rrrjsonloadsJSONDecodeErrorr ValueErrorrprediction_loss_onlyddp_broadcast_buffers parallel_moder NOT_DISTRIBUTED output_dir DISTRIBUTEDdataloader_drop_last)self __class__s r(r4#BaseTrainingArguments.__post_init__|s - .-2H H  ,1B1Ba1G$($5$5!$(!t$++U33d>O>O8URU8UZ^ZkZkorZr$($5$5!$%! 2[TU]5n[US5(aUS [US5(aUS U$)Nrr)r3to_dictcallable)rAtraining_args_dictrBs r(rEBaseTrainingArguments.to_dictsJ"W_. &7 8 8"?3 &'DE F F"#@A!!r') rr@r;rrr:rrr+r.)__name__ __module__ __qualname____firstlineno____doc___VALID_DICT_FIELDSrr__annotations__r BATCH_SAMPLERrr PROPORTIONALrdictrrr+r4rE__static_attributes__ __classcell__)rBs@r(rrs'Z LQ d LG Hin++v?Z6[iMe )66&JsAt  " SX P SNOAF X A="'n ] "L,\-|""r'r) __future__rr6loggingcollections.abcr dataclassesrrtypingrpackaging.versionrr$ transformersr TransformersTrainingArgumentsr r%transformers.training_argsr "sentence_transformers.base.samplerr r rr getLoggerrIr/rr&r'r(r`s^" $(4K<3   8 $ G"9G" G"r'