rhbddlmZddlmZmZddlmZddlmZGddejZ y)) annotations)Tensornn) CrossEncoder)fullnamecPeZdZejfdfd ZddZdZxZS)MSELossc t|||_||_t j di||_t|jts8t|jjdt|jd|jjdk7r9t|jjd|jjdy)a 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 /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/sentence_transformers/cross_encoder/losses/MSELoss.pyrzMSELoss.__init__ sz  * ,V, $**l3>>**+,++/ +;*>**+,((, (=(='>oO  &ct|dk7rtdt|dtt|d|d}|jj |ddd}|j |jj|jd i|djd }|j|}|j||j}|S) NzMMSELoss expects a dataset with two non-label columns, but got a dataset with z columns.rr Tpt)padding truncationreturn_tensorsr ) lenrlistzipr tokenizertodeviceviewrrfloat)rinputslabelspairstokenslogitslosss rforwardzMSELoss.forwardXs v;! _`cdj`k_lluv SF1I./%%  &   $**##$%f%a(--b1##F+}}VV\\^4 rc0dt|jiS)Nr)rr)rs rget_config_dictzMSELoss.get_config_dictks Xd&8&89  r)rrrz nn.ModulereturnNone)r,zlist[list[str]]r-rr5r) r __module__ __qualname__rIdentityrr2r4 __classcell__)rs@rr r sGRr{{}L\& rr N) __future__rtorchrr0sentence_transformers.cross_encoder.CrossEncoderrsentence_transformers.utilrModuler r rrr@s#"I/e biie r