**Augmented SBERT** is a data augmentation strategy for pairwise sentence scoring that uses a [BERT](https://paperswithcode.com/method/bert) cross-encoder to improve the performance for the [SBERT](https://paperswithcode.com/method/sbert) bi-encoders. Given a pre-trained, well-performing crossencoder, we sample sentence pairs according to a certain sampling strategy and label these using the cross-encoder. We call these weakly labeled examples the silver dataset and they will be merged with the gold training dataset. We then train the bi-encoder on this extended training dataset.

**PAR Transformer** is a [Transformer](https://paperswithcode.com/methods/category/transformers) model that uses 63% fewer [self-attention blocks](https://paperswithcode.com/method/scaled), replacing them with [feed-forward blocks](https://paperswithcode.com/method/position-wise-feed-forward-layer), while retaining test accuracies. It is based on the [Transformer-XL](https://paperswithcode.com/method/transformer-xl) architecture and uses [neural architecture search](https://paperswithcode.com/task/architecture-search) to find an an efficient pattern of blocks in the transformer architecture.

PAR Transformer

Pay Attention when Required

Augmented SBERT

Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks

**LeNet** is a classic convolutional neural network employing the use of convolutions, pooling and fully connected layers. It was used for the handwritten digit recognition task with the MNIST dataset. The architectural design served as inspiration for future networks such as [AlexNet](https://paperswithcode.com/method/alexnet) and [VGG](https://paperswithcode.com/method/vgg)..

[code](https://github.com/Elman295/Paper_with_code/blob/main/LeNet_5_Pytorch.ipynb)

Source	Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com