from __future__ import annotations
from enum import Enum
from typing import Any, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""The metric for the contrastive loss"""
EUCLIDEAN = lambda x, y: F.pairwise_distance(x, y, p=2)
MANHATTAN = lambda x, y: F.pairwise_distance(x, y, p=1)
COSINE_DISTANCE = lambda x, y: 1 - F.cosine_similarity(x, y)
[文档]
class ContrastiveLoss(nn.Module):
def __init__(
self,
model: SentenceTransformer,
distance_metric=SiameseDistanceMetric.COSINE_DISTANCE,
margin: float = 0.5,
size_average: bool = True,
) -> None:
"""
Contrastive loss. Expects as input two texts and a label of either 0 or 1. If the label == 1, then the distance between the
two embeddings is reduced. If the label == 0, then the distance between the embeddings is increased.
Args:
model: SentenceTransformer model
distance_metric: Function that returns a distance between
two embeddings. The class SiameseDistanceMetric contains
pre-defined metrices that can be used
margin: Negative samples (label == 0) should have a distance
of at least the margin value.
size_average: Average by the size of the mini-batch.
References:
* Further information: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
* `Training Examples > Quora Duplicate Questions <../../examples/training/quora_duplicate_questions/README.html>`_
Requirements:
1. (anchor, positive/negative) pairs
Inputs:
+-----------------------------------------------+------------------------------+
| Texts | Labels |
+===============================================+==============================+
| (anchor, positive/negative) pairs | 1 if positive, 0 if negative |
+-----------------------------------------------+------------------------------+
Relations:
- :class:`OnlineContrastiveLoss` is similar, but uses hard positive and hard negative pairs.
It often yields better results.
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."],
"label": [1, 0],
})
loss = losses.ContrastiveLoss(model)
trainer = SentenceTransformerTrainer(
model=model,
train_dataset=train_dataset,
loss=loss,
)
trainer.train()
"""
super().__init__()
self.distance_metric = distance_metric
self.margin = margin
self.model = model
self.size_average = size_average
def get_config_dict(self) -> dict[str, Any]:
distance_metric_name = self.distance_metric.__name__
for name, value in vars(SiameseDistanceMetric).items():
if value == self.distance_metric:
distance_metric_name = f"SiameseDistanceMetric.{name}"
break
return {"distance_metric": distance_metric_name, "margin": self.margin, "size_average": self.size_average}
def forward(self, sentence_features: Iterable[dict[str, Tensor]], labels: Tensor) -> Tensor:
reps = [self.model(sentence_feature)["sentence_embedding"] for sentence_feature in sentence_features]
assert len(reps) == 2
rep_anchor, rep_other = reps
distances = self.distance_metric(rep_anchor, rep_other)
losses = 0.5 * (
labels.float() * distances.pow(2) + (1 - labels).float() * F.relu(self.margin - distances).pow(2)
)
return losses.mean() if self.size_average else losses.sum()
@property
def citation(self) -> str:
return """
@inproceedings{hadsell2006dimensionality,
author={Hadsell, R. and Chopra, S. and LeCun, Y.},
booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
title={Dimensionality Reduction by Learning an Invariant Mapping},
year={2006},
volume={2},
number={},
pages={1735-1742},
doi={10.1109/CVPR.2006.100}
}
"""