Sparse-Encoder Losses (SPLADE)

All losses live in sentence_transformers.sparse_encoder.losses.

This reference targets the SPLADE architecture (Transformer + SpladePooling). The sparse-encoder package also exports CSRLoss and CSRReconstructionLoss for the CSR architecture (Transformer + Pooling + SparseAutoEncoder); those are out of scope here — see the sbert.net docs if you're training a CSR model.

Choosing a loss means (a) pick a base loss (contrastive, regression, distillation) and (b) wrap it in SpladeLoss to add FLOPS regularization.

Top-line decision table

The core wrapper: SpladeLoss

SpladeLoss adds FLOPS regularization on top of another sparse loss. FLOPS regularization penalizes non-zero activations, keeping embeddings genuinely sparse.

loss = SpladeLoss(
    model=model,
    loss=SparseMultipleNegativesRankingLoss(model=model),
    query_regularizer_weight=5e-5,
    document_regularizer_weight=3e-5,
)

• query_regularizer_weight: how much to penalize non-zero terms in query embeddings.
• document_regularizer_weight: same for documents.
• Typical range: 1e-5 to 1e-4. Higher = sparser embeddings, lower recall; lower = denser, possibly better recall.
• SparseEncoderTrainer automatically registers a SpladeRegularizerWeightSchedulerCallback whenever the loss is a SpladeLoss. The callback ramps the weights from 0 up to the target over the first ~33% of training; the default shape is SchedulerType.QUADRATIC (not linear). The ramp length and shape are configured on the callback (SpladeRegularizerWeightSchedulerCallback(loss=..., warmup_ratio=..., scheduler_type=...)), not on SpladeLoss; to override, instantiate the callback yourself and pass it via callbacks=[...]. This ramp is important; starting with full regularization from step 0 kills learning.

Use CachedSpladeLoss for the GradCache variant.

Contrastive losses (no labels)

SparseMultipleNegativesRankingLoss

Sparse analog of bi-encoder MNRL. In-batch contrastive.

inner = SparseMultipleNegativesRankingLoss(model=model)
loss = SpladeLoss(model=model, loss=inner, query_regularizer_weight=5e-5, document_regularizer_weight=3e-5)

• Always wrap in SpladeLoss for SPLADE architectures.
• Set batch_sampler=BatchSamplers.NO_DUPLICATES on training args.

SparseTripletLoss

Classic triplet margin loss on explicit (anchor, positive, negative).

Labeled regression losses

SparseCoSENTLoss

Pairwise ranking loss for (text1, text2, score). Mirrors bi-encoder CoSENTLoss.

SparseCosineSimilarityLoss

MSE on cosine similarity. Simpler, usually worse than CoSENT.

SparseAnglELoss

Angle-based loss in complex space. Alternative to CoSENT.

Distillation losses

SparseMSELoss

Embedding MSE. Student sparse embedding should match teacher embedding.

• Data: (text, teacher_embedding).
• Teacher can be a dense bi-encoder or another sparse model.

SparseMarginMSELoss

Margin MSE from a cross-encoder teacher.

• Data: (query, positive, negative, score_diff) where score_diff = teacher_score(query, positive) - teacher_score(query, negative).
• Typical recipe for training SPLADE from cross-encoder labels (ms-marco distillation).
• Wrap in SpladeLoss(model, loss=SparseMarginMSELoss(model), ...) for SPLADE.

SparseDistillKLDivLoss

Listwise KL-div distillation — student's softmax distribution over candidates should match teacher's.

Independent regularizer

FlopsLoss

Standalone FLOPS regularizer. Usually you use this via SpladeLoss, not directly.

For regularizer-weight tuning and dense-output recovery, see troubleshooting.md ("SPLADE embeddings are dense"). MLM-head requirement: base_model_selection.md (SPARSE section). Active-dim sparsity targets and how to monitor them: evaluators_sparse_encoder.md (Sparsity tracking).

Gotchas

• SparseMultipleNegativesRankingLoss without SpladeLoss wrapping on a SPLADE model: no FLOPS regularization -> dense outputs defeating the purpose of SPLADE. Always wrap.
• CachedSpladeLoss + gradient_checkpointing=True: crash. Pick one.
• Starting training with full FLOPS regularization at step 0: the model outputs zero everywhere and gets stuck. The built-in scheduler avoids this — don't override it unless you know why.
• query_regularizer_weight == document_regularizer_weight: usually wrong. Queries should be sparser than documents (fewer terms per query). Since higher regularization drives more zeros, give the query weight the larger value. query_regularizer_weight=5e-5, document_regularizer_weight=3e-5 is a good starting ratio.