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class SentenceTransformer (nn .Sequential ):
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def __init__ (
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self ,
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- model_name_or_path : str | None = None ,
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- modules : Iterable [nn .Module ] | None = None ,
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- prompts : dict [str , str ] | None = None ,
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- default_prompt_name : str | None = None ,
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- similarity_fn_name : str | SimilarityFunction | None = None ,
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- cache_folder : str | None = None ,
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+ model_name_or_path : str = None ,
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+ modules : Iterable [nn .Module ] = None ,
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+ prompts : dict [str , str ] = None ,
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+ default_prompt_name : str = None ,
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+ similarity_fn_name : str | SimilarityFunction = None ,
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+ cache_folder : str = None ,
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trust_remote_code : bool = False ,
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- revision : str | None = None ,
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+ revision : str = None ,
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local_files_only : bool = False ,
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- token : bool | str | None = None ,
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- truncate_dim : int | None = None ,
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- model_kwargs : dict [str , Any ] | None = None ,
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- tokenizer_kwargs : dict [str , Any ] | None = None ,
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- config_kwargs : dict [str , Any ] | None = None ,
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+ token : bool | str = None ,
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+ truncate_dim : int = None ,
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+ model_kwargs : dict [str , Any ] = None ,
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+ tokenizer_kwargs : dict [str , Any ] = None ,
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+ config_kwargs : dict [str , Any ] = None ,
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):
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self .prompts = prompts or {}
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self .default_prompt_name = default_prompt_name
@@ -193,8 +193,8 @@ def _load_module_class_from_ref(
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class_ref : str ,
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model_name_or_path : str ,
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trust_remote_code : bool ,
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- revision : str | None ,
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- model_kwargs : dict [str , Any ] | None ,
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+ revision : str ,
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+ model_kwargs : dict [str , Any ],
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) -> nn .Module :
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# If the class is from sentence_transformers, we can directly import it,
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# otherwise, we try to import it dynamically, and if that fails, we fall back to the default import
@@ -206,14 +206,14 @@ def _load_module_class_from_ref(
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def _load_sbert_model (
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self ,
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model_name_or_path : str ,
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- token : bool | str | None ,
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- cache_folder : str | None ,
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- revision : str | None = None ,
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+ token : bool | str ,
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+ cache_folder : str ,
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+ revision : str = None ,
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trust_remote_code : bool = False ,
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local_files_only : bool = False ,
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- model_kwargs : dict [str , Any ] | None = None ,
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- tokenizer_kwargs : dict [str , Any ] | None = None ,
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- config_kwargs : dict [str , Any ] | None = None ,
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+ model_kwargs : dict [str , Any ] = None ,
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+ tokenizer_kwargs : dict [str , Any ] = None ,
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+ config_kwargs : dict [str , Any ] = None ,
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) -> dict [str , nn .Module ]:
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"""
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Loads a full SentenceTransformer model using the modules.json file.
@@ -385,14 +385,14 @@ def _load_sbert_model(
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def _load_auto_model (
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self ,
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model_name_or_path : str ,
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- token : bool | str | None ,
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- cache_folder : str | None ,
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- revision : str | None = None ,
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+ token : bool | str ,
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+ cache_folder : str ,
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+ revision : str = None ,
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trust_remote_code : bool = False ,
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local_files_only : bool = False ,
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- model_kwargs : dict [str , Any ] | None = None ,
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- tokenizer_kwargs : dict [str , Any ] | None = None ,
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- config_kwargs : dict [str , Any ] | None = None ,
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+ model_kwargs : dict [str , Any ] = None ,
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+ tokenizer_kwargs : dict [str , Any ] = None ,
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+ config_kwargs : dict [str , Any ] = None ,
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) -> list [nn .Module ]:
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"""
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Creates a simple Transformer + Mean Pooling model and returns the modules
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