[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

Multilingual Automatic Speech Recognition with word-level timestamps and confidence

KoBERT와 CRF로 만든 한국어 개체명인식기 (BERT+CRF based Named Entity Recognition model for Korean)

Plot the vector graph of attention based text visualisation

Neat (Neural Attention) Vision, is a visualization tool for the attention mechanisms of deep-learning models for Natural Language Processing (NLP) tasks. (framework-agnostic)

Train and visualize Hierarchical Attention Networks

Comparatively fine-tuning pretrained BERT models on downstream, text classification tasks with different architectural configurations in PyTorch.

Visualizing query-key interactions in language + vision transformers

Visualization for simple attention and Google's multi-head attention.

Summary of Transformer applications for computer vision tasks.

🚀 Cross attention map tools for huggingface/diffusers

my codes for learning attention mechanism

(ECCV2020) Tensorflow implementation of A Generic Visualization Approach for Convolutional Neural Networks

Lightweight visualization tool for neural attention mechanisms

Implemented image caption generation method propossed in Show, Attend, and Tell paper using the Fastai framework to describe the content of images. Achieved 24 BLEU score for Beam search size of 5. Designed a Web application for model deployment using the Flask framework.

attention mechanism in keras, like Dense and RNN...

Easy-to-read implementation of self-supervised learning using vision transformer and knowledge distillation with no labels - DINO 😃

PyTorch implementation of the End-to-End Memory Network with attention layer vizualisation support.

A Pytorch implementation of the paper 'Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering'