l^(hrddlmZddlZddlZddlmZddlmZmZddl m Z GddejZ y)) annotationsN) OrderedDict)Tensornn)import_from_stringcPeZdZddfd Zd dZd dZdZd dZedZ xZ S) Asymc||_||_t}|jD]?\}}t |t s|g}t |D]\}}|||dzt|z<At|%|y)a This model allows to create asymmetric SentenceTransformer models, that apply different models depending on the specified input key. In the below example, we create two different Dense models for 'query' and 'doc'. Text that is passed as {'query': 'My query'} will be passed along along the first Dense model, and text that will be passed as {'doc': 'My document'} will use the other Dense model. Note, that when you call encode(), that only inputs of the same type can be encoded. Mixed-Types cannot be encoded. Example: :: from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses from datasets import Dataset # Load a SentenceTransformer model (pretrained or not), and add an Asym module model = SentenceTransformer("microsoft/mpnet-base") dim = model.get_sentence_embedding_dimension() asym_model = models.Asym({ 'query': [models.Dense(dim, dim)], 'doc': [models.Dense(dim, dim)] }) model.add_module("asym", asym_model) train_dataset = Dataset.from_dict({ "query": ["is toprol xl the same as metoprolol?", "are eyes always the same size?"], "answer": ["Metoprolol succinate is also known by the brand name Toprol XL.", "The eyes are always the same size from birth to death."], }) # This mapper turns normal texts into a dictionary mapping Asym keys to the text def mapper(sample): return { "question": {"query": sample["question"]}, "answer": {"doc": sample["answer"]}, } train_dataset = train_dataset.map(mapper) loss = losses.MultipleNegativesRankingLoss(model) trainer = SentenceTransformerTrainer( model=model, train_dataset=train_dataset, loss=loss, ) trainer.train() # For inference, you can pass dictionaries with the Asym keys: model.encode([ {'query': 'how long do you have to wait to apply for cerb?'}, {'query': '<3 what does this symbol mean?'}, {'doc': 'The definition of <3 is "Love".'}] ) Note: These models are not necessarily stronger than non-asymmetric models. Rudimentary experiments indicate that non-Asym models perform better in most cases. Args: sub_modules: Dict in the format str -> List[models]. The models in the specified list will be applied for input marked with the respective key. allow_empty_key: If true, inputs without a key can be processed. If false, an exception will be thrown if no key is specified. -N) sub_modulesallow_empty_keyritems isinstancelist enumeratestrsuper__init__) selfr r ordered_dictnamemodelsidxmodel __class__s _/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/sentence_transformers/models/Asym.pyrz Asym.__init__ sB'."} '--/ C/$u+2F2FF). X&(,U (>(> H%%,,X6  7 7 ,113 #OHek3x=AJ KK T 2 JJz " # "'',,{M:C& Q UY II(!0#4d6J6J"K     s ()EE#c t|dts tdd}|D]2}tt |j \}}||}||k(r2J|j |dj|fi|S)z-Tokenizes a text and maps tokens to token-idsrzDAsym. model requires that texts are passed as dicts: {'key': 'text'}N)rdictAttributeErrornextiterrr tokenize)rtextskwargs module_keylookupr#texts rrMz Asym.tokenizes%(D) !gh h  *F!$v||~"67NHd!% z) ))  * 8t +A.77HHHrcttjj|d5}t j |}dddi}dj D]D\}}t|}|j tjj||}|||<Fi}|dj D](\} } g|| <| D]}|| j||*t|fi|d} | S#1swYxYw)Nr+r/r0r1) r>r9r:r;r?loadrrr8r ) input_pathfInconfigmodulesrE model_type module_classmodulerDkey_name models_listrs rrTz Asym.loads "'',,z=9 : $cYYs^F $$*7O$9$9$; ' Hj-j9L!&&rww||J'IJF &GH  ' %+K%8%>%>%@ D !Hk(*OH %' D)001BC D D _=|(<= ! $ $s C55C>)T)r zdict[str, list[nn.Module]]r bool)r"zdict[str, Tensor])returnint)rNz!list[str] | list[tuple[str, str]]) r6r7 __qualname__rr$r&r=rM staticmethodrT __classcell__)rs@rr r s3K'Z : Irr ) __future__rr?r9 collectionsrtorchrrsentence_transformers.utilr Sequentialr rrrjs)" #9\2==\r