+ QV-jN p^RIHt^RIHt^RIHtHtHt]!]!RRR7R4!RR ]44tR #) )Any)add_end_docstrings) GenericTensorPipelinebuild_pipeline_init_argsTF) has_tokenizersupports_binary_outputa tokenize_kwargs (`dict`, *optional*): Additional dictionary of keyword arguments passed along to the tokenizer. return_tensors (`bool`, *optional*): If `True`, returns a tensor according to the specified framework, otherwise returns a list.c~aa]tRt^toRtRtRtRtRtR Rlt V3RlRlt Rt R Rlt V3R lV3R llt R tVtV;t#)FeatureExtractionPipelinea Feature extraction pipeline uses no model head. This pipeline extracts the hidden states from the base transformer, which can be used as features in downstream tasks. Example: ```python >>> from transformers import pipeline >>> extractor = pipeline(model="google-bert/bert-base-uncased", task="feature-extraction") >>> result = extractor("This is a simple test.", return_tensors=True) >>> result.shape # This is a tensor of shape [1, sequence_length, hidden_dimension] representing the input string. torch.Size([1, 8, 768]) ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier: `"feature-extraction"`. All models may be used for this pipeline. See a list of all models, including community-contributed models on [huggingface.co/models](https://huggingface.co/models). FTc dVf/pVeRV9d \R4hWR&Tp/pVeW6R&V/V3#)N truncationz\truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)return_tensors) ValueError)selfr tokenize_kwargsrkwargspreprocess_paramspostprocess_paramss&&&&, z/Volumes/fast/ai/experiments/ui-tars-smoke/.venv/lib/python3.14/site-packages/transformers/pipelines/feature_extraction.py_sanitize_parameters.FeatureExtractionPipeline._sanitize_parameters-s^  " O  !. r-7L )+  %3A/ 0 "&888c6<V^8dQhRS[S[S[3,/#)return)dictstrr)format __classdict__s"r __annotate__&FeatureExtractionPipeline.__annotate__@stCrJsD&CC4NkI1I1I1r