from __future__ import annotations
from typing import Any, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
[文档]
class CosineSimilarityLoss(nn.Module):
def __init__(
self,
model: SentenceTransformer,
loss_fct: nn.Module = nn.MSELoss(),
cos_score_transformation: nn.Module = nn.Identity(),
) -> None:
"""
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``.
Args:
model: SentenceTransformer model
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``
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]`
Inputs:
+--------------------------------+------------------------+
| Texts | Labels |
+================================+========================+
| (sentence_A, sentence_B) pairs | float similarity score |
+--------------------------------+------------------------+
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.
Example:
::
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer, losses
from datasets import Dataset
model = SentenceTransformer("microsoft/mpnet-base")
train_dataset = Dataset.from_dict({
"sentence1": ["It's nice weather outside today.", "He drove to work."],
"sentence2": ["It's so sunny.", "She walked to the store."],
"score": [1.0, 0.3],
})
loss = losses.CosineSimilarityLoss(model)
trainer = SentenceTransformerTrainer(
model=model,
train_dataset=train_dataset,
loss=loss,
)
trainer.train()
"""
super().__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) -> 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.float().view(-1))
def get_config_dict(self) -> dict[str, Any]:
return {"loss_fct": fullname(self.loss_fct)}