Python Spacy-transformers Resources

Flexible interface for high performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra. What is Lightning Tran

PyTorch impelementations of BERT-based Spelling Error Correction Models

PyTorch impelementations of BERT-based Spelling Error Correction Models. 基于BERT的文本纠错模型，使用PyTorch实现。

Allennlp Integration for Shiba allennlp-shiab-model is a Python library that provides AllenNLP integration for shiba-model. SHIBA is an approximate re

LoFTR-with-train-script LoFTR:Detector-Free Local Feature Matching with Transformers CVPR 2021 (with train script --- unofficial ---). About Megadepth

Video Swin Transformer By Ze Liu*, Jia Ning*, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin and Han Hu. This repo is the official implementation of "V

LETR: Line Segment Detection Using Transformers without Edges Introduction This repository contains the official code and pretrained models for Line S

3D Human Pose Estimation with Spatial and Temporal Transformers This repo is the official implementation for 3D Human Pose Estimation with Spatial and

Out-of-distribution Generalization Investigation on Vision Transformers This repository contains PyTorch evaluation code for Delving Deep into the Gen

easySpeech easySpeech is an open source python wrapper for google speech to text api that doesn't require PyAaudio(So you specially windows user don't

RASP Setup Mac or Linux Run ./setup.sh . It will create a python3 virtual environment and install the dependencies for RASP. It will also try to insta

文字是人类用以记录和表达的最基本工具，也是信息传播的重要媒介。透过文字与符号，我们可以追寻人类文明的起源，可以传播知识与经验，读懂文字是认识与了解的第一步。对于人工智能而言，它的核心问题之一就是认知，而认知的核心则是语义理解。

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers Figure 1: Performance of SegFormer-B0 to SegFormer-B5. Project page

Graphormer By Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng*, Guolin Ke, Di He*, Yanming Shen and Tie-Yan Liu. This repo is the official impl

Motionformer This is an official pytorch implementation of paper Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers. In this rep

Smaller Multilingual Transformers This repository shares smaller versions of multilingual transformers that keep the same representations offered by t

Improving-Adversarial-Transferability-of-Vision-Transformers Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Fahad Khan, Fatih Porikli arxiv link A

Less is More: Pay Less Attention in Vision Transformers Official PyTorch implementation of Less is More: Pay Less Attention in Vision Transformers. By

CATs: Semantic Correspondence with Transformers For more information, check out the paper on [arXiv]. Training with different backbones and evaluation

We challenge a common assumption underlying most supervised deep learning: that a model makes a prediction depending only on its parameters and the features of a single input. To this end, we introduce a general-purpose deep learning architecture that takes as input the entire dataset instead of processing one datapoint at a time.

DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification Created by Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, Ch

Intriguing Properties of Vision Transformers Muzammal Naseer, Kanchana Ranasinghe, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, & Ming-Hsuan Yang P

Official PyTorch Implementation for HOTR: End-to-End Human-Object Interaction Detection with Transformers (CVPR'2021, Oral Presentation) HOTR: End-to-

Introduction This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolut

Quickly train T5 models in just 3 lines of code + ONNX support simpleT5 is built on top of PyTorch-lightning ⚡️ and Transformers 🤗 that lets you quic

CrossViT : Cross-Attention Multi-Scale Vision Transformer for Image Classification This is an unofficial PyTorch implementation of CrossViT: Cross-Att

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

Introduction This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolut

MeshTransformer ✨ This is our research code of End-to-End Human Pose and Mesh Reconstruction with Transformers. MEsh TRansfOrmer is a simple yet effec

IVR-Chatbot Achievements 🏆 Team Uhtred won the Maverick 2.0 Bot-a-thon 2021 organized by AbInbev India. ❓ Problem Statement As we all know that, lot

Medical Image Segmentation using Squeeze-and-Expansion Transformers Introduction This repository contains the code of the IJCAI'2021 paper 'Medical Im

Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation This repository is the pytorch implementation of our paper: Hierarchical Cr

Vision Transformers are Robust Learners This repository contains the code for the paper Vision Transformers are Robust Learners by Sayak Paul* and Pin

Fork from https://github.com/huggingface/transformers/tree/86d5fb0b360e68de46d40265e7c707fe68c8015b/examples/pytorch/language-modeling at 2021.05.17.

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks ACL 2020 Microsoft Research [Paper] [Video] Releasing [XtremeDistilTransf

Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

This repository contains PyTorch code for Robust Vision Transformers.

TrackFormer: Multi-Object Tracking with Transformers This repository provides the official implementation of the TrackFormer: Multi-Object Tracking wi

Do You Even Need Attention? A Stack of Feed-Forward Layers Does Surprisingly Well on ImageNet Paper/Report TL;DR We replace the attention layer in a v

Reformer, the Efficient Transformer, in Pytorch This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH

jiant is an NLP toolkit The multitask and transfer learning toolkit for natural language processing research Why should I use jiant? jiant supports mu

keras implement of transformers for humans

Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.

This is the project page for the paper: ISTR: End-to-End Instance Segmentation via Transformers, Jie Hu, Liujuan Cao, Yao Lu, ShengChuan Zhang, Yan Wa

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

Self-Supervised Vision Transformers with DINO PyTorch implementation and pretrained models for DINO. For details, see Emerging Properties in Self-Supe

Geometry-Free View Synthesis: Transformers and no 3D Priors Geometry-Free View Synthesis: Transformers and no 3D Priors Robin Rombach*, Patrick Esser*

Twins: Revisiting the Design of Spatial Attention in Vision Transformers Very recently, a variety of vision transformer architectures for dense predic

SceneFormer: Indoor Scene Generation with Transformers Initial code release for the Sceneformer paper, contains models, train and test scripts for the

CoaT: Co-Scale Conv-Attentional Image Transformers Introduction This repository contains the official code and pretrained models for CoaT: Co-Scale Co

SpikeX is a collection of pipes ready to be plugged in a spaCy pipeline. It aims to help in building knowledge extraction tools with almost-zero effort.

VideoGPT: Video Generation using VQ-VAE and Transformers [Paper][Website][Colab][Gradio Demo] We present VideoGPT: a conceptually simple architecture

Labelled data remains a scarce resource in many practical NLP scenarios. This is especially the case when working with resource-poor languages (or text domains), or when using task-specific labels without pre-existing datasets. The only available option is often to collect and annotate texts by hand, which is expensive and time-consuming.

Compact Transformers Preprint Link: Escaping the Big Data Paradigm with Compact Transformers By Ali Hassani[1]*, Steven Walton[1]*, Nikhil Shah[1], Ab

TransFuser This repository contains the code for the CVPR 2021 paper Multi-Modal Fusion Transformer for End-to-End Autonomous Driving. If you find our

Perceiver This Python package implements Perceiver: General Perception with Iterative Attention by Andrew Jaegle in TensorFlow. This model builds on t

PRTR: Pose Recognition with Cascade Transformers Introduction This repository is the official implementation for Pose Recognition with Cascade Transfo

We will first introduce the how to run the IPython notebook demo by downloading our pretrained models. Then, we will introduce how to run our training and evaluation code.

Cross Transformers - Pytorch (wip) Implementation of Cross Transformer for spatially-aware few-shot transfer, in Pytorch Install $ pip install cross-t

TransGanFormer (wip) Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GansFormer and TransGan paper. I

Cutoff: A Simple Data Augmentation Approach for Natural Language This repository contains source code necessary to reproduce the results presented in

pair-emnlp2020 Official repository for the paper: Xinyu Hua and Lu Wang: PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long

Implementation of various Vision Transformers I found interesting

LoFTR: Detector-Free Local Feature Matching with Transformers Project Page | Paper LoFTR: Detector-Free Local Feature Matching with Transformers Jiami

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Top2Vec is an algorithm for topic modeling and semantic search. It automatically detects topics present in text and generates jointly embedded topic, document and word vectors.

The Most Important Thing. Our code is developed based on: LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Group-Free 3D Object Detection via Transformers By Ze Liu, Zheng Zhang, Yue Cao, Han Hu, Xin Tong. This repo is the official implementation of "Group-

LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval This repository contains source code and pre-trained/fine-tun

Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning This is the PyTorch companion code for the paper: A

PyTorch Implementation of Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers 1 Using Colab Please notic

Code Transformer This is an official PyTorch implementation of the CodeTransformer model proposed in: D. Zügner, T. Kirschstein, M. Catasta, J. Leskov

CvT: Introducing Convolutions to Vision Transformers Pytorch implementation of CvT: Introducing Convolutions to Vision Transformers Usage: img = torch

Guide: Finetune GPT2-XL (1.5 Billion Parameters) and GPT-NEO (2.7 Billion Parameters) on a single 16 GB VRAM V100 Google Cloud instance with Huggingfa

STAM - Pytorch Implementation of STAM (Space Time Attention Model), yet another pure and simple SOTA attention model that bests all previous models in

3D Human Pose Estimation with Spatial and Temporal Transformers This repo is the official implementation for 3D Human Pose Estimation with Spatial and

Official PyTorch code for Transformers Solve Limited Receptive Field for Monocular Depth Prediction. Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe,

Performer - Pytorch An implementation of Performer, a linear attention-based transformer variant with a Fast Attention Via positive Orthogonal Random

Reformer, the Efficient Transformer, in Pytorch This is a Pytorch implementation of Reformer https://openreview.net/pdf?id=rkgNKkHtvB It includes LSH

An easier way to build neural search on the cloud Jina is a deep learning-powered search framework for building cross-/multi-modal search systems (e.g

Vision Transformers for Dense Prediction This repository contains code and models for our paper: Vision Transformers for Dense Prediction René Ranftl,

BossNAS This repository contains PyTorch evaluation code, retraining code and pretrained models of our paper: BossNAS: Exploring Hybrid CNN-transforme

Adaptive Transformers in RL Official implementation of Adaptive Transformers in RL In this work we replicate several results from Stabilizing Transfor

DL Translate A deep learning-based translation library built on Huggingface transformers and Facebook's mBART-Large 💻 GitHub Repository 📚 Documentat

Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch

Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network)

Mesh Transformer Jax A haiku library using the new(ly documented) xmap operator in Jax for model parallelism of transformers. See enwik8_example.py fo

VisTR: End-to-End Video Instance Segmentation with Transformers This is the official implementation of the VisTR paper: Installation We provide instru

universal-computation Overview Official codebase for Pretrained Transformers as Universal Computation Engines. Contains demo notebook and scripts to r

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers This is the official PyTorch implementation and models for UP-DETR paper: @a

XPandas (extended Pandas) implements 1D and 2D data containers for storing type-heterogeneous tabular data of any type, and encapsulates feature extra

COCO LM Pretraining (wip) Implementation of COCO-LM, Correcting and Contrasting Text Sequences for Language Model Pretraining, in Pytorch. They were a

Omninet - Pytorch Implementation of OmniNet, Omnidirectional Representations from Transformers, in Pytorch. The authors propose that we should be atte

DaCy: A SpaCy NLP Pipeline for Danish DaCy is a Danish preprocessing pipeline trained in SpaCy. At the time of writing it has achieved State-of-the-Ar

GANsformer: Generative Adversarial Transformers Drew A. Hudson* & C. Lawrence Zitnick *I wish to thank Christopher D. Manning for the fruitf

Transformer in Transformer Implementation of Transformer in Transformer, pixel level attention paired with patch level attention for image c

Medical-Transformer Pytorch Code for the paper "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" About this repo: This repo

SETR - Pytorch Since the original paper (Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.) has no official

TransUNet This repo holds code for TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Usage