ó — óh|ãór•SSKJr SSKJr SSKJrJr SSKJr SSK J r J r "SS\R5r g ) é)Ú annotations)ÚIterable)ÚTensorÚnn)ÚSentenceTransformeré)ÚBatchHardTripletLossÚ$BatchHardTripletLossDistanceFunctioncóz^•\rSrSr\R S4SU4SjjjrS SjrS Sjr\ S Sj5r Sr U=r $) ÚBatchAllTripletLossé écóF>•[TU]5 XlX0lX lg)aæ BatchAllTripletLoss takes a batch with (sentence, label) pairs and computes the loss for all possible, valid triplets, i.e., anchor and positive must have the same label, anchor and negative a different label. The labels must be integers, with same label indicating sentences from the same class. Your train dataset must contain at least 2 examples per label class. Args: model: SentenceTransformer model distance_metric: Function that returns a distance between two embeddings. The class SiameseDistanceMetric contains pre-defined metrics that can be used. margin: Negative samples should be at least margin further apart from the anchor than the positive. References: * Source: https://github.com/NegatioN/OnlineMiningTripletLoss/blob/master/online_triplet_loss/losses.py * Paper: In Defense of the Triplet Loss for Person Re-Identification, https://arxiv.org/abs/1703.07737 * Blog post: https://omoindrot.github.io/triplet-loss Requirements: 1. Each sentence must be labeled with a class. 2. Your dataset must contain at least 2 examples per labels class. Inputs: +------------------+--------+ | Texts | Labels | +==================+========+ | single sentences | class | +------------------+--------+ Recommendations: - Use ``BatchSamplers.GROUP_BY_LABEL`` (:class:`docs `) to ensure that each batch contains 2+ examples per label class. Relations: * :class:`BatchHardTripletLoss` uses only the hardest positive and negative samples, rather than all possible, valid triplets. * :class:`BatchHardSoftMarginTripletLoss` uses only the hardest positive and negative samples, rather than all possible, valid triplets. Also, it does not require setting a margin. * :class:`BatchSemiHardTripletLoss` uses only semi-hard triplets, valid triplets, rather than all possible, valid triplets. Example: :: from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses from datasets import Dataset model = SentenceTransformer("microsoft/mpnet-base") # E.g. 0: sports, 1: economy, 2: politics train_dataset = Dataset.from_dict({ "sentence": [ "He played a great game.", "The stock is up 20%", "They won 2-1.", "The last goal was amazing.", "They all voted against the bill.", ], "label": [0, 1, 0, 0, 2], }) loss = losses.BatchAllTripletLoss(model) trainer = SentenceTransformerTrainer( model=model, train_dataset=train_dataset, loss=loss, ) trainer.train() N)ÚsuperÚ__init__Úsentence_embedderÚtriplet_marginÚdistance_metric)ÚselfÚmodelrÚmarginÚ __class__s €Új/home/james-whalen/.local/lib/python3.13/site-packages/sentence_transformers/losses/BatchAllTripletLoss.pyrÚBatchAllTripletLoss.__init__ s#ø€ôT ‰ÑÔØ!&ÔØ$ÔØ.ÕócóR•URUS5SnURX#5$)NrÚsentence_embedding)rÚbatch_all_triplet_loss)rÚsentence_featuresÚlabelsÚreps rÚforwardÚBatchAllTripletLoss.forward\s/€Ø×$Ñ$Ð%6°qÑ%9Ó:Ð;OÑPˆØ×*Ñ*¨6Ó7Ð7rcóJ•URU5nURS5nURS5nXE- UR-n[R"U5nUR 5U-nSXfS:'XfS:„nUR S5n UR5U S-- nU$)a Build the triplet loss over a batch of embeddings. We generate all the valid triplets and average the loss over the positive ones. Args: labels: labels of the batch, of size (batch_size,) embeddings: tensor of shape (batch_size, embed_dim) margin: margin for triplet loss squared: Boolean. If true, output is the pairwise squared euclidean distance matrix. If false, output is the pairwise euclidean distance matrix. Returns: Label_Sentence_Triplet: scalar tensor containing the triplet loss érrg¼‰Ø—²Òœ<)rÚ unsqueezerr Úget_triplet_maskÚfloatÚsizeÚsum) rr Ú embeddingsÚ pairwise_distÚanchor_positive_distÚanchor_negative_distÚ triplet_lossÚmaskÚvalid_tripletsÚnum_positive_tripletss rrÚ*BatchAllTripletLoss.batch_all_triplet_loss`s½€ð×,Ñ,¨ZÓ8ˆ à,×6Ñ6°qÓ9ÐØ,×6Ñ6°qÓ9Ðð ,ÑBÀT×EXÑEXÑXˆ ô$×4Ò4°VÓ<ˆØ—z‘z“| lÑ2ˆ ð*+ˆ  AÑ%Ñ&ð&°UÑ&:Ñ;ˆØ .× 3Ñ 3°AÓ 6Ðð $×'Ñ'Ó)Ð-BÀUÑ-JÑKˆ àÐrcó•g)Na @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ©)rs rÚcitationÚBatchAllTripletLoss.citation‹s€ð r)rrr)rrrr(ÚreturnÚNone)rzIterable[dict[str, Tensor]]r rr8r)r rr+rr8r)r8Ústr) Ú__name__Ú __module__Ú __qualname__Ú__firstlineno__r Úeucledian_distancerr"rÚpropertyr6Ú__static_attributes__Ú __classcell__)rs@rr r s\ø†ð=×OÑOØð M/à"ðM/ðð M/ð ÷ M/ðM/ô^8ô)ðVó óö rr N) Ú __future__rÚcollections.abcrÚtorchrrÚ)sentence_transformers.SentenceTransformerrr r ÚModuler r5rrÚrHs&ðÝ"å$çåIç\ôJ˜"Ÿ)™)õJr