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125 lines
4.6 KiB
125 lines
4.6 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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from sentence_transformers.util import pairwise_cos_sim, pairwise_euclidean_sim, pairwise_manhattan_sim
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class TripletDistanceMetric(Enum):
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"""The metric for the triplet loss"""
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COSINE = lambda x, y: 1 - pairwise_cos_sim(x, y)
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EUCLIDEAN = lambda x, y: pairwise_euclidean_sim(x, y)
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MANHATTAN = lambda x, y: pairwise_manhattan_sim(x, y)
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class TripletLoss(nn.Module):
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def __init__(
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self, model: SentenceTransformer, distance_metric=TripletDistanceMetric.EUCLIDEAN, triplet_margin: float = 5
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) -> None:
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"""
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This class implements triplet loss. Given a triplet of (anchor, positive, negative),
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the loss minimizes the distance between anchor and positive while it maximizes the distance
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between anchor and negative. It compute the following loss function:
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``loss = max(||anchor - positive|| - ||anchor - negative|| + margin, 0)``.
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Margin is an important hyperparameter and needs to be tuned respectively.
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Args:
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model: SentenceTransformerModel
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distance_metric: Function to compute distance between two
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embeddings. The class TripletDistanceMetric contains
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common distance metrices that can be used.
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triplet_margin: The negative should be at least this much
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further away from the anchor than the positive.
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References:
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- For further details, see: https://en.wikipedia.org/wiki/Triplet_loss
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Requirements:
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1. (anchor, positive, negative) triplets
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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) triplets | none |
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+---------------------------------------+--------+
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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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"anchor": ["It's nice weather outside today.", "He drove to work."],
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"positive": ["It's so sunny.", "He took the car to the office."],
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"negative": ["It's quite rainy, sadly.", "She walked to the store."],
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})
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loss = losses.TripletLoss(model=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.distance_metric = distance_metric
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self.triplet_margin = triplet_margin
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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 CoSENT loss from embeddings.
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Args:
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embeddings: List of embeddings
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Returns:
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Loss value
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"""
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rep_anchor, rep_pos, rep_neg = embeddings
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distance_pos = self.distance_metric(rep_anchor, rep_pos)
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distance_neg = self.distance_metric(rep_anchor, rep_neg)
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losses = F.relu(distance_pos - distance_neg + self.triplet_margin)
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return losses.mean()
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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(TripletDistanceMetric).items():
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if value == self.distance_metric:
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distance_metric_name = f"TripletDistanceMetric.{name}"
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break
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return {"distance_metric": distance_metric_name, "triplet_margin": self.triplet_margin}
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@property
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def citation(self) -> str:
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return """
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@misc{hermans2017defense,
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title={In Defense of the Triplet Loss for Person Re-Identification},
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author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
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year={2017},
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eprint={1703.07737},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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"""
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