NgLddlmZddlmZmZmZGddejZdS)) annotations)SentenceTransformerlossesutilc:eZdZd d fd Zed d ZxZS) AnglELoss4@modelrscalefloatreturnNonecdt||tjdS)av This class implements AnglE (Angle Optimized) loss. This is a modification of :class:`CoSENTLoss`, designed to address the following issue: The cosine function's gradient approaches 0 as the wave approaches the top or bottom of its form. This can hinder the optimization process, so AnglE proposes to instead optimize the angle difference in complex space in order to mitigate this effect. It expects that each of the InputExamples consists of a pair of texts and a float valued label, representing the expected similarity score between the pair. It computes the following loss function: ``loss = logsum(1+exp(s(k,l)-s(i,j))+exp...)``, where ``(i,j)`` and ``(k,l)`` are any of the input pairs in the batch such that the expected similarity of ``(i,j)`` is greater than ``(k,l)``. The summation is over all possible pairs of input pairs in the batch that match this condition. This is the same as CoSENTLoss, with a different similarity function. Args: model: SentenceTransformerModel scale: Output of similarity function is multiplied by scale value. Represents the inverse temperature. References: - For further details, see: https://arxiv.org/abs/2309.12871v1 Requirements: - Sentence pairs with corresponding similarity scores in range of the similarity function. Default is [-1,1]. Inputs: +--------------------------------+------------------------+ | Texts | Labels | +================================+========================+ | (sentence_A, sentence_B) pairs | float similarity score | +--------------------------------+------------------------+ Relations: - :class:`CoSENTLoss` is AnglELoss with ``pairwise_cos_sim`` as the metric, rather than ``pairwise_angle_sim``. - :class:`CosineSimilarityLoss` seems to produce a weaker training signal than ``CoSENTLoss`` or ``AnglELoss``. Example: :: from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses from datasets import Dataset model = SentenceTransformer("microsoft/mpnet-base") train_dataset = Dataset.from_dict({ "sentence1": ["It's nice weather outside today.", "He drove to work."], "sentence2": ["It's so sunny.", "She walked to the store."], "score": [1.0, 0.3], }) loss = losses.AnglELoss(model) trainer = SentenceTransformerTrainer( model=model, train_dataset=train_dataset, loss=loss, ) trainer.train() )similarity_fctN)super__init__rpairwise_angle_sim)selfr r __class__s b/var/www/html/ai-engine/env/lib/python3.11/site-packages/sentence_transformers/losses/AnglELoss.pyrzAnglELoss.__init__s.z d6MNNNNNstrcdS)Nz @misc{li2023angleoptimized, title={AnglE-optimized Text Embeddings}, author={Xianming Li and Jing Li}, year={2023}, eprint={2309.12871}, archivePrefix={arXiv}, primaryClass={cs.CL} } )rs rcitationzAnglELoss.citationFs   r)r )r rr r r r)r r)__name__ __module__ __qualname__rpropertyr __classcell__)rs@rrrsj=O=O=O=O=O=O=O~   X     rrN) __future__rsentence_transformersrrr CoSENTLossrrrrr$sv""""""CCCCCCCCCCKKKKK!KKKKKr