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99 lines
4.2 KiB
99 lines
4.2 KiB
from __future__ import annotations
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from collections.abc import Iterable
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from typing import Any
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import torch
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from torch import Tensor, nn
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from sentence_transformers.SentenceTransformer import SentenceTransformer
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from sentence_transformers.util import fullname
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class CosineSimilarityLoss(nn.Module):
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def __init__(
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self,
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model: SentenceTransformer,
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loss_fct: nn.Module = nn.MSELoss(),
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cos_score_transformation: nn.Module = nn.Identity(),
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) -> None:
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"""
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CosineSimilarityLoss expects that the InputExamples consists of two texts and a float label. It computes the
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vectors ``u = model(sentence_A)`` and ``v = model(sentence_B)`` and measures the cosine-similarity between the two.
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By default, it minimizes the following loss: ``||input_label - cos_score_transformation(cosine_sim(u,v))||_2``.
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Args:
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model: SentenceTransformer model
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loss_fct: Which pytorch loss function should be used to
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compare the ``cosine_similarity(u, v)`` with the
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input_label? By default, MSE is used: ``||input_label -
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cosine_sim(u, v)||_2``
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cos_score_transformation: The cos_score_transformation
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function is applied on top of cosine_similarity. By
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default, the identify function is used (i.e. no change).
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References:
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- `Training Examples > Semantic Textual Similarity <../../../examples/sentence_transformer/training/sts/README.html>`_
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Requirements:
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1. Sentence pairs with corresponding similarity scores in range `[0, 1]`
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Inputs:
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+--------------------------------+------------------------+
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| Texts | Labels |
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+================================+========================+
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| (sentence_A, sentence_B) pairs | float similarity score |
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+--------------------------------+------------------------+
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Relations:
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- :class:`CoSENTLoss` seems to produce a stronger training signal than CosineSimilarityLoss. In our experiments, CoSENTLoss is recommended.
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- :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.
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Example:
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::
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from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses
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from datasets import Dataset
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model = SentenceTransformer("microsoft/mpnet-base")
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train_dataset = Dataset.from_dict({
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"sentence1": ["It's nice weather outside today.", "He drove to work."],
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"sentence2": ["It's so sunny.", "She walked to the store."],
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"score": [1.0, 0.3],
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})
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loss = losses.CosineSimilarityLoss(model)
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trainer = SentenceTransformerTrainer(
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model=model,
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train_dataset=train_dataset,
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loss=loss,
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)
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trainer.train()
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"""
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super().__init__()
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self.model = model
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self.loss_fct = loss_fct
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self.cos_score_transformation = cos_score_transformation
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def forward(self, sentence_features: Iterable[dict[str, Tensor]], labels: Tensor) -> Tensor:
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embeddings = [self.model(sentence_feature)["sentence_embedding"] for sentence_feature in sentence_features]
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return self.compute_loss_from_embeddings(embeddings, labels)
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def compute_loss_from_embeddings(self, embeddings: list[Tensor], labels: Tensor) -> Tensor:
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"""
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Compute the CosineSimilarity loss from embeddings.
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Args:
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embeddings: List of embeddings
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labels: Labels indicating the similarity scores of the pairs
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Returns:
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Loss value
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"""
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output = self.cos_score_transformation(torch.cosine_similarity(embeddings[0], embeddings[1]))
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return self.loss_fct(output, labels.float().view(-1))
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def get_config_dict(self) -> dict[str, Any]:
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return {"loss_fct": fullname(self.loss_fct)}
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