Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers.

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Deep Learning Contra-OOD
Overview

Contra-OOD

Code for EMNLP 2021 paper Contrastive Out-of-Distribution Detection for Pretrained Transformers.

Requirements

Dataset

Most datasets used in paper are automatically downloaded by the datasets package. Instructions on downloading sst2 and multi30k are provided in readme.txt under the data folder.

Training and Evaluation

Finetune the PLM with the following command:

>> python run.py --task_name task

The task_name parameter can take sst2, imdb, trec, or 20ng. The training loss and evaluation results on the test set are synced to the wandb dashboard.

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Comments
  • Problem of Table 2

    Problem of Table 2

    The Table 2 seems confused. Why L_{margin} + MSP are not in green on SST2 dataset and in green on IMDb dataset? You write 'Results where the contrastive loss improves OOD detection on both evaluation metrics are highlighted in green.' And lower FAR95 is better.

    opened by wjczf123 2
  • Mask is always zero in Margin-based Contrastive Loss

    Mask is always zero in Margin-based Contrastive Loss

    Reference: https://github.com/wzhouad/Contra-OOD/blob/main/model.py#L40

    I have question about the implementation of Margin-based Contrastive Loss

    mask = (labels.unsqueeze(1) == labels.unsqueeze(0)).float()

    If the batch size is 64, the labels variable have a shape of [64] When the above code performs, ([64, 1] == [1, 64]).float() → [64, 64], which is exact 2D diagonal matrix.

    mask = mask - torch.diag(torch.diag(mask))

    But the problem is on the second line of code. If torch.diag(mask) performs, the result has a shape of [64] that is one-filled vector: $[1, 1, 1, ...]$ Therefore, the result of torch.diag(torch.diag(mask)) is exactly same with the mask, which is exact 2D diagonal matrix. Furthermore, if you subtract the result from mask, eventually the mask is always zero-filled matrix. Eventually, the mask variable have no power for gradient descending.

    Is this really on your purpose?

    I thought the mask variable is used for distinguishing $P(i)$ and $N(i)$ in equation. Is this right? Or am I missing a point?

    opened by OrigamiDream 1
  • 关于loss的一点疑问？

    关于loss的一点疑问？

    非常好的工作！ 请问一下论文中实现的Supervised Contrastive Learning Loss https://github.com/wzhouad/Contra-OOD/blob/2a1d63a61c8b03efdc27ca08b22f5fab2bc6001d/model.py#L46 和 原论文中提供的SCL代码https://github.com/HobbitLong/SupContrast/blob/master/losses.py有什么差异吗？ 看起来本文实现的更为简洁？

    opened by topDreamer 1
Owner
Wenxuan Zhou
Ph.D. student at University of Southern California
Wenxuan Zhou
GitHub
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