Loss Overview

Loss functions play a critical role in the performance of your fine-tuned model. Sadly, there is no “one size fits all” loss function. Ideally, this overview should help narrow down your choice of loss function(s) by matching them to your data formats.

Note: you can often convert one training data format into another, allowing more loss functions to be viable for your scenario. For example, (sentence_A, sentence_B) pairs with class labels can be converted into (anchor, positive, negative) triplets by sampling sentences with the same or different classes.

Texts	Labels	Appropriate Loss Functions
single sentences	class	BatchAllTripletLoss BatchHardSoftMarginTripletLoss BatchHardTripletLoss BatchSemiHardTripletLoss
single sentences	none	ContrastiveTensionLoss DenoisingAutoEncoderLoss
(anchor, anchor) pairs	none	ContrastiveTensionLossInBatchNegatives
(damaged_sentence, original_sentence) pairs	none	DenoisingAutoEncoderLoss
(sentence_A, sentence_B) pairs	class	SoftmaxLoss
(anchor, positive) pairs	none	CachedMultipleNegativesRankingLoss MultipleNegativesRankingLoss MultipleNegativesSymmetricRankingLoss MegaBatchMarginLoss CachedGISTEmbedLoss GISTEmbedLoss
(anchor, positive/negative) pairs	1 if positive, 0 if negative	ContrastiveLoss OnlineContrastiveLoss
(sentence_A, sentence_B) pairs	float similarity score	CoSENTLoss AnglELoss CosineSimilarityLoss
(anchor, positive, negative) triplets	none	CachedMultipleNegativesRankingLoss MultipleNegativesRankingLoss TripletLoss CachedGISTEmbedLoss GISTEmbedLoss

Loss modifiers

These loss functions can be seen as loss modifiers: they work on top of standard loss functions, but apply those loss functions in different ways to try and instil useful properties into the trained embedding model.

For example, models trained with MatryoshkaLoss produce embeddings whose size can be truncated without notable losses in performance, and models trained with AdaptiveLayerLoss still perform well when you remove model layers for faster inference.

Texts	Labels	Appropriate Loss Functions
any	any	MatryoshkaLoss AdaptiveLayerLoss Matryoshka2dLoss

Distillation

These loss functions are specifically designed to be used when distilling the knowledge from one model into another. For example, when finetuning a small model to behave more like a larger & stronger one, or when finetuning a model to become multi-lingual.

Texts	Labels	Appropriate Loss Functions
single sentences	model sentence embeddings	MSELoss
(query, passage_one, passage_two) triplets	gold_sim(query, passage_one) - gold_sim(query, passage_two)	MarginMSELoss

Commonly used Loss Functions

In practice, not all loss functions get used equally often. The most common scenarios are:

• (anchor, positive) pairs without any labels: MultipleNegativesRankingLoss is commonly used to train the top performing embedding models. This data is often relatively cheap to obtain, and the models are generally very performant. CachedMultipleNegativesRankingLoss is often used to increase the batch size, resulting in superior performance.

• (sentence_A, sentence_B) pairs with a float similarity score: CosineSimilarityLoss is traditionally used a lot, though more recently CoSENTLoss and AnglELoss are used as drop-in replacements with superior performance.