CosineSimilarityLoss.py

import torch
from torch import nn, Tensor
from typing import Iterable, Dict
from ..SentenceTransformer import SentenceTransformer


class CosineSimilarityLoss(nn.Module):
    def __init__(self, model: SentenceTransformer, loss_fct=nn.MSELoss(), cos_score_transformation=nn.Identity()):
        """
        CosineSimilarityLoss expects that the InputExamples consists of two texts and a float label. It computes the
        vectors ``u = model(sentence_A)`` and ``v = model(sentence_B)`` and measures the cosine-similarity between the two.
        By default, it minimizes the following loss: ``||input_label - cos_score_transformation(cosine_sim(u,v))||_2``.

        :param model: SentenceTransformer model
        :param loss_fct: Which pytorch loss function should be used to compare the ``cosine_similarity(u, v)`` with the input_label?
            By default, MSE is used: ``||input_label - cosine_sim(u, v)||_2``
        :param cos_score_transformation: The cos_score_transformation function is applied on top of cosine_similarity.
            By default, the identify function is used (i.e. no change).

        References:
            - `Training Examples > Semantic Textual Similarity <../../examples/training/sts/README.html>`_

        Requirements:
            1. Sentence pairs with corresponding similarity scores in range `[0, 1]`

        Relations:
            - :class:`CoSENTLoss` seems to produce a stronger training signal than CosineSimilarityLoss. In our experiments, CoSENTLoss is recommended.
            - :class:`AnglELoss` is :class:`CoSENTLoss` with ``pairwise_angle_sim`` as the metric, rather than ``pairwise_cos_sim``. It also produces a stronger training signal than CosineSimilarityLoss.

        Inputs:
            +--------------------------------+------------------------+
            | Texts                          | Labels                 |
            +================================+========================+
            | (sentence_A, sentence_B) pairs | float similarity score |
            +--------------------------------+------------------------+

        Example:
            ::

                from sentence_transformers import SentenceTransformer, InputExample, losses
                from torch.utils.data import DataLoader

                model = SentenceTransformer('distilbert-base-nli-mean-tokens')
                train_examples = [
                    InputExample(texts=['My first sentence', 'My second sentence'], label=0.8),
                    InputExample(texts=['Another pair', 'Unrelated sentence'], label=0.3)
                ]
                train_batch_size = 1
                train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=train_batch_size)
                train_loss = losses.CosineSimilarityLoss(model=model)

                model.fit(
                    [(train_dataloader, train_loss)],
                    epochs=10,
                )
        """
        super(CosineSimilarityLoss, self).__init__()
        self.model = model
        self.loss_fct = loss_fct
        self.cos_score_transformation = cos_score_transformation

    def forward(self, sentence_features: Iterable[Dict[str, Tensor]], labels: Tensor):
        embeddings = [self.model(sentence_feature)["sentence_embedding"] for sentence_feature in sentence_features]
        output = self.cos_score_transformation(torch.cosine_similarity(embeddings[0], embeddings[1]))
        return self.loss_fct(output, labels.view(-1))