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120 lines
4.8 KiB
120 lines
4.8 KiB
from __future__ import annotations
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from collections.abc import Iterable
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from enum import Enum
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from typing import Any
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import torch.nn.functional as F
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from torch import Tensor, nn
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from sentence_transformers.SentenceTransformer import SentenceTransformer
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class SiameseDistanceMetric(Enum):
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"""The metric for the contrastive loss"""
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EUCLIDEAN = lambda x, y: F.pairwise_distance(x, y, p=2)
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MANHATTAN = lambda x, y: F.pairwise_distance(x, y, p=1)
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COSINE_DISTANCE = lambda x, y: 1 - F.cosine_similarity(x, y)
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class ContrastiveLoss(nn.Module):
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def __init__(
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self,
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model: SentenceTransformer,
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distance_metric=SiameseDistanceMetric.COSINE_DISTANCE,
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margin: float = 0.5,
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size_average: bool = True,
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) -> None:
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"""
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Contrastive loss. Expects as input two texts and a label of either 0 or 1. If the label == 1, then the distance between the
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two embeddings is reduced. If the label == 0, then the distance between the embeddings is increased.
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Args:
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model: SentenceTransformer model
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distance_metric: Function that returns a distance between
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two embeddings. The class SiameseDistanceMetric contains
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pre-defined metrices that can be used
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margin: Negative samples (label == 0) should have a distance
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of at least the margin value.
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size_average: Average by the size of the mini-batch.
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References:
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* Further information: https://www.researchgate.net/publication/4246277_Dimensionality_Reduction_by_Learning_an_Invariant_Mapping
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* `Training Examples > Quora Duplicate Questions <../../../examples/sentence_transformer/training/quora_duplicate_questions/README.html>`_
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Requirements:
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1. (anchor, positive/negative) pairs
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Inputs:
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+-----------------------------------------------+------------------------------+
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| Texts | Labels |
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+===============================================+==============================+
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| (anchor, positive/negative) pairs | 1 if positive, 0 if negative |
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+-----------------------------------------------+------------------------------+
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Relations:
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- :class:`OnlineContrastiveLoss` is similar, but uses hard positive and hard negative pairs.
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It often yields better results.
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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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"label": [1, 0],
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})
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loss = losses.ContrastiveLoss(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.distance_metric = distance_metric
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self.margin = margin
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self.model = model
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self.size_average = size_average
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def get_config_dict(self) -> dict[str, Any]:
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distance_metric_name = self.distance_metric.__name__
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for name, value in vars(SiameseDistanceMetric).items():
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if value == self.distance_metric:
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distance_metric_name = f"SiameseDistanceMetric.{name}"
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break
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return {"distance_metric": distance_metric_name, "margin": self.margin, "size_average": self.size_average}
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def forward(self, sentence_features: Iterable[dict[str, Tensor]], labels: Tensor) -> Tensor:
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reps = [self.model(sentence_feature)["sentence_embedding"] for sentence_feature in sentence_features]
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assert len(reps) == 2
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rep_anchor, rep_other = reps
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distances = self.distance_metric(rep_anchor, rep_other)
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losses = 0.5 * (
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labels.float() * distances.pow(2) + (1 - labels).float() * F.relu(self.margin - distances).pow(2)
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)
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return losses.mean() if self.size_average else losses.sum()
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@property
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def citation(self) -> str:
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return """
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@inproceedings{hadsell2006dimensionality,
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author={Hadsell, R. and Chopra, S. and LeCun, Y.},
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booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
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title={Dimensionality Reduction by Learning an Invariant Mapping},
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year={2006},
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volume={2},
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number={},
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pages={1735-1742},
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doi={10.1109/CVPR.2006.100}
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}
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"""
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