hbSSKJr SSKJrJr SSKJr SSKJr "SS\R5r g)) annotations)Tensornn) CrossEncoder)fullnamec`^\rSrSr\R "54SU4SjjjrSSjrSrSr U=r $)MSELoss c >[TU]5 XlX l[R "S0UD6Ul[UR[5(d8[URRS[UR5S35eURRS:wa9[URRSURRS35eg)a9 Computes the MSE loss between the computed query-passage score and a target query-passage score. This loss is used to distill a cross-encoder model from a teacher cross-encoder model or gold labels. Args: model (:class:`~sentence_transformers.cross_encoder.CrossEncoder`): A CrossEncoder model to be trained. activation_fn (:class:`~torch.nn.Module`): Activation function applied to the logits before computing the loss. **kwargs: Additional keyword arguments passed to the underlying :class:`torch.nn.MSELoss`. .. note:: Be mindful of the magnitude of both the labels and what the model produces. If the teacher model produces logits with Sigmoid to bound them to [0, 1], then you may wish to use a Sigmoid activation function in the loss. References: - Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation: https://arxiv.org/abs/2010.02666 - `Cross Encoder > Training Examples > Distillation <../../../examples/cross_encoder/training/distillation/README.html>`_ Requirements: 1. Your model must be initialized with `num_labels = 1` (a.k.a. the default) to predict one class. 2. Usually uses a finetuned CrossEncoder teacher M in a knowledge distillation setup. Inputs: +-----------------------------------------+-----------------------------+-------------------------------+ | Texts | Labels | Number of Model Output Labels | +=========================================+=============================+===============================+ | (sentence_A, sentence_B) pairs | similarity score | 1 | +-----------------------------------------+-----------------------------+-------------------------------+ Relations: - :class:`MarginMSELoss` is similar to this loss, but with a margin through a negative pair. Example: :: from sentence_transformers.cross_encoder import CrossEncoder, CrossEncoderTrainer, losses from datasets import Dataset student_model = CrossEncoder("microsoft/mpnet-base") teacher_model = CrossEncoder("cross-encoder/ms-marco-MiniLM-L12-v2") train_dataset = Dataset.from_dict({ "query": ["What are pandas?", "What is the capital of France?"], "answer": ["Pandas are a kind of bear.", "The capital of France is Paris."], }) def compute_labels(batch): return { "label": teacher_model.predict(list(zip(batch["query"], batch["answer"]))) } train_dataset = train_dataset.map(compute_labels, batched=True) loss = losses.MSELoss(student_model) trainer = CrossEncoderTrainer( model=student_model, train_dataset=train_dataset, loss=loss, ) trainer.train() z? expects a model of type CrossEncoder, but got a model of type .z; expects a model with 1 output label, but got a model with z output labels.N)super__init__model activation_fnrr loss_fct isinstancer ValueError __class____name__type num_labels)selfrrkwargsrs l/home/james-whalen/.local/lib/python3.13/site-packages/sentence_transformers/cross_encoder/losses/MSELoss.pyrMSELoss.__init__ sz  * ,V, $**l33>>**+,++/ +;*>**+,((, (=(='>oO  &c[U5S:wa[S[U5S35e[[USUS55nURR USSSS9nUR URR5 UR"S 0UD6SRS 5nURU5nURXRR55nU$) NzMMSELoss expects a dataset with two non-label columns, but got a dataset with z columns.rr Tpt)padding truncationreturn_tensorsr) lenrlistzipr tokenizertodeviceviewrrfloat)rinputslabelspairstokenslogitslosss rforwardMSELoss.forwardXs v;! _`cdj`k_lluv SF1I./%%  &   $**##$%f%a(--b1##F+}}V\\^4 rc0S[UR50$)Nr)rr)rs rget_config_dictMSELoss.get_config_dictks Xd&8&89  r)rrr)rrrz nn.ModulereturnNone)r.zlist[list[str]]r/rr9r) r __module__ __qualname____firstlineno__rIdentityrr4r7__static_attributes__ __classcell__)rs@rr r s(GI{{}LL\&  rr N) __future__rtorchrr0sentence_transformers.cross_encoder.CrossEncoderrsentence_transformers.utilrModuler rrrrFs#"I/e biie r