1620 Repositories
Python transformers-models Libraries
The aim is to contain multiple models for materials discovery under a common interface
Aviary The aviary contains: - roost, - wren, cgcnn. The aim is to contain multiple models for materials discovery under a common interface Environment
Django models and endpoints for working with large images -- tile serving
Django Large Image Models and endpoints for working with large images in Django -- specifically geared towards geospatial tile serving. DISCLAIMER: th
Training deep models using anime, illustration images.
animeface deep models for anime images. Datasets anime-face-dataset Anime faces collected from Getchu.com. Based on Mckinsey666's dataset. 63.6K image
A PyTorch-based model pruning toolkit for pre-trained language models
English | 中文说明 TextPruner是一个为预训练语言模型设计的模型裁剪工具包,通过轻量、快速的裁剪方法对模型进行结构化剪枝,从而实现压缩模型体积、提升模型速度。 其他相关资源: 知识蒸馏工具TextBrewer:https://github.com/airaria/TextBrewe
A library that allows for inference on probabilistic models
Bean Machine Overview Bean Machine is a probabilistic programming language for inference over statistical models written in the Python language using
The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".
This code was written during the course of my Bachelor thesis Classification of Human Whole-Body Motion using Hidden Markov Models. Some things might
Training PyTorch models with differential privacy
Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the cli
Official code for "On the Frequency Bias of Generative Models", NeurIPS 2021
Frequency Bias of Generative Models Generator Testbed Discriminator Testbed This repository contains official code for the paper On the Frequency Bias
Yuno is context based search engine for anime.
Yuno yuno.mp4 Table of Contents Introduction Power Of Yuno Try Yuno How Yuno was created? References Introduction Yuno is a context based search engin
Build Low Code Automated Tensorflow, What-IF explainable models in just 3 lines of code.
Build Low Code Automated Tensorflow explainable models in just 3 lines of code.
scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms.
Sklearn-genetic-opt scikit-learn models hyperparameters tuning and feature selection, using evolutionary algorithms. This is meant to be an alternativ
Official code repository for "Exploring Neural Models for Query-Focused Summarization"
Query-Focused Summarization Official code repository for "Exploring Neural Models for Query-Focused Summarization" This is a work in progress. Expect
Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains
Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains This is an accompanying repository to the ICAIL 2021 pap
"Learning and Analyzing Generation Order for Undirected Sequence Models" in Findings of EMNLP, 2021
undirected-generation-dev This repo contains the source code of the models described in the following paper "Learning and Analyzing Generation Order f
Ensembling Off-the-shelf Models for GAN Training
Vision-aided GAN video (3m) | website | paper Can the collective knowledge from a large bank of pretrained vision models be leveraged to improve GAN t
Code for the ICCV'21 paper "Context-aware Scene Graph Generation with Seq2Seq Transformers"
ICCV'21 Context-aware Scene Graph Generation with Seq2Seq Transformers Authors: Yichao Lu*, Himanshu Rai*, Cheng Chang*, Boris Knyazev†, Guangwei Yu,
Code for paper Multitask-Finetuning of Zero-shot Vision-Language Models
Code for paper Multitask-Finetuning of Zero-shot Vision-Language Models
Ensembling Off-the-shelf Models for GAN Training
Data-Efficient GANs with DiffAugment project | paper | datasets | video | slides Generated using only 100 images of Obama, grumpy cats, pandas, the Br
Pytorch modules for paralel models with same architecture. Ideal for multi agent-based systems
WideLinears Pytorch parallel Neural Networks A package of pytorch modules for fast paralellization of separate deep neural networks. Ideal for agent-b
Fit models to your data in Python with Sherpa.
Table of Contents Sherpa License How To Install Sherpa Using Anaconda Using pip Building from source History Release History Sherpa Sherpa is a modeli
Label data using HuggingFace's transformers and automatically get a prediction service
Label Studio for Hugging Face's Transformers Website • Docs • Twitter • Join Slack Community Transfer learning for NLP models by annotating your textu
This repository contains the code, models and datasets discussed in our paper "Few-Shot Question Answering by Pretraining Span Selection"
Splinter This repository contains the code, models and datasets discussed in our paper "Few-Shot Question Answering by Pretraining Span Selection", to
Model parallel transformers in JAX and Haiku
Table of contents Mesh Transformer JAX Updates Pretrained Models GPT-J-6B Links Acknowledgments License Model Details Zero-Shot Evaluations Architectu
ByT5: Towards a token-free future with pre-trained byte-to-byte models
ByT5: Towards a token-free future with pre-trained byte-to-byte models ByT5 is a tokenizer-free extension of the mT5 model. Instead of using a subword
GANformer: Generative Adversarial Transformers
GANformer: Generative Adversarial Transformers Drew A. Hudson* & C. Lawrence Zitnick Update: We released the new GANformer2 paper! *I wish to thank Ch
CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation
CPT This repository contains code and checkpoints for CPT. CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Gener
FastFormers - highly efficient transformer models for NLU
FastFormers FastFormers provides a set of recipes and methods to achieve highly efficient inference of Transformer models for Natural Language Underst
The code for the Subformer, from the EMNLP 2021 Findings paper: "Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers", by Machel Reid, Edison Marrese-Taylor, and Yutaka Matsuo
Subformer This repository contains the code for the Subformer. To help overcome this we propose the Subformer, allowing us to retain performance while
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
English | 简体中文 | 繁體中文 | 한국어 State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained models
Pre-training with Extracted Gap-sentences for Abstractive SUmmarization Sequence-to-sequence models
PEGASUS library Pre-training with Extracted Gap-sentences for Abstractive SUmmarization Sequence-to-sequence models, or PEGASUS, uses self-supervised
Fine-tuning scripts for evaluating transformer-based models on KLEJ benchmark.
The KLEJ Benchmark Baselines The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language und
Dense Passage Retriever - is a set of tools and models for open domain Q&A task.
Dense Passage Retrieval Dense Passage Retrieval (DPR) - is a set of tools and models for state-of-the-art open-domain Q&A research. It is based on the
Sinkhorn Transformer - Practical implementation of Sparse Sinkhorn Attention
Sinkhorn Transformer This is a reproduction of the work outlined in Sparse Sinkhorn Attention, with additional enhancements. It includes a parameteriz
DeLighT: Very Deep and Light-Weight Transformers
DeLighT: Very Deep and Light-weight Transformers This repository contains the source code of our work on building efficient sequence models: DeFINE (I
This repository contains the code for running the character-level Sandwich Transformers from our ACL 2020 paper on Improving Transformer Models by Reordering their Sublayers.
Improving Transformer Models by Reordering their Sublayers This repository contains the code for running the character-level Sandwich Transformers fro
Examples of using sparse attention, as in "Generating Long Sequences with Sparse Transformers"
Status: Archive (code is provided as-is, no updates expected) Update August 2020: For an example repository that achieves state-of-the-art modeling pe
Code for the paper "Language Models are Unsupervised Multitask Learners"
Status: Archive (code is provided as-is, no updates expected) gpt-2 Code and models from the paper "Language Models are Unsupervised Multitask Learner
Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
Tensor2Tensor Tensor2Tensor, or T2T for short, is a library of deep learning models and datasets designed to make deep learning more accessible and ac
An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
GPT Neo 🎉 1T or bust my dudes 🎉 An implementation of model & data parallel GPT3-like models using the mesh-tensorflow library. If you're just here t
Awesome Treasure of Transformers Models Collection
💁 Awesome Treasure of Transformers Models for Natural Language processing contains papers, videos, blogs, official repo along with colab Notebooks. 🛫☑️
Using VapourSynth with super resolution models and speeding them up with TensorRT.
VSGAN-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Using NVIDIA/Torch-TensorRT combined wi
Face and Body Tracking for VRM 3D models on the web.
Kalidoface 3D - Face and Full-Body tracking for Vtubing on the web! A sequal to Kalidoface which supports Live2D avatars, Kalidoface 3D is a web app t
💃 VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena
💃 VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena.
Text Classification in Turkish Texts with Bert
You can watch the details of the project on my youtube channel Project Interface Project Second Interface Goal= Correctly guessing the classification
Formulae is a Python library that implements Wilkinson's formulas for mixed-effects models.
formulae formulae is a Python library that implements Wilkinson's formulas for mixed-effects models. The main difference with other implementations li
Powerful unsupervised domain adaptation method for dense retrieval.
Powerful unsupervised domain adaptation method for dense retrieval
Convert onnx models to pytorch.
onnx2torch onnx2torch is an ONNX to PyTorch converter. Our converter: Is easy to use – Convert the ONNX model with the function call convert; Is easy
Robbing the FED: Directly Obtaining Private Data in Federated Learning with Modified Models
Robbing the FED: Directly Obtaining Private Data in Federated Learning with Modified Models This repo contains a barebones implementation for the atta
The python SDK for Eto, the AI focused data platform for teams bringing AI models to production
Eto Labs Python SDK This is the python SDK for Eto, the AI focused data platform for teams bringing AI models to production. The python SDK makes it e
Code for Temporally Abstract Partial Models
Code for Temporally Abstract Partial Models Accompanies the code for the experimental section of the paper: Temporally Abstract Partial Models, Khetar
Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
WECHSEL Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. arXiv: https://arx
Experiments on continual learning from a stream of pretrained models.
Ex-model CL Ex-model continual learning is a setting where a stream of experts (i.e. model's parameters) is available and a CL model learns from them
Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models.
Statutory Interpretation Data Set This repository contains the data set created for the following research papers: Savelka, Jaromir, and Kevin D. Ashl
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
keytotext Idea is to build a model which will take keywords as inputs and generate sentences as outputs. Potential use case can include: Marketing Sea
AdamW optimizer for bfloat16 models in pytorch.
Image source AdamW optimizer for bfloat16 models in pytorch. Bfloat16 is currently an optimal tradeoff between range and relative error for deep netwo
Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.
WECHSEL Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. arXiv: https://arx
MLReef is an open source ML-Ops platform that helps you collaborate, reproduce and share your Machine Learning work with thousands of other users.
The collaboration platform for Machine Learning MLReef is an open source ML-Ops platform that helps you collaborate, reproduce and share your Machine
Integrate GraphQL with your Pydantic models
graphene-pydantic A Pydantic integration for Graphene. Installation pip install "graphene-pydantic" Examples Here is a simple Pydantic model: import u
A repository with exploration into using transformers to predict DNA ↔ transcription factor binding
Transcription Factor binding predictions with Attention and Transformers A repository with exploration into using transformers to predict DNA ↔ transc
Using image super resolution models with vapoursynth and speeding them up with TensorRT
vs-RealEsrganAnime-tensorrt-docker Using image super resolution models with vapoursynth and speeding them up with TensorRT. Also a docker image since
Code for paper: "Spinning Language Models for Propaganda-As-A-Service"
Spinning Language Models for Propaganda-As-A-Service This is the source code for the Arxiv version of the paper. You can use this Google Colab to expl
A FAIR dataset of TCV experimental results for validating edge/divertor turbulence models.
TCV-X21 validation for divertor turbulence simulations Quick links Intro Welcome to TCV-X21. We're glad you've found us! This repository is designed t
Pydantic models for pywttr and aiopywttr.
Pydantic models for pywttr and aiopywttr.
A minimal, standalone viewer for 3D animations stored as stop-motion sequences of individual .obj mesh files.
ObjSequenceViewer V0.5 A minimal, standalone viewer for 3D animations stored as stop-motion sequences of individual .obj mesh files. Installation: pip
PyTorch implementation of normalizing flow models
PyTorch implementation of normalizing flow models
(Preprint) Official PyTorch implementation of "How Do Vision Transformers Work?"
(Preprint) Official PyTorch implementation of "How Do Vision Transformers Work?"
Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts
t5-japanese Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts. The following is a list of models that
PyAbsorp is a python module that has the main focus to help estimate the Sound Absorption Coefficient.
This is a package developed to be use to find the Sound Absorption Coefficient through some implemented models, like Biot-Allard, Johnson-Champoux and
SeqAttack: a framework for adversarial attacks on token classification models
A framework for adversarial attacks against token classification models
Users can free try their models on SIDD dataset based on this code
SIDD benchmark 1 Train python train.py If you want to train your network, just modify the yaml in the options folder. 2 Validation python validation.p
PyTorch implementation of a collections of scalable Video Transformer Benchmarks.
PyTorch implementation of Video Transformer Benchmarks This repository is mainly built upon Pytorch and Pytorch-Lightning. We wish to maintain a colle
Easy Language Model Pretraining leveraging Huggingface's Transformers and Datasets
Easy Language Model Pretraining leveraging Huggingface's Transformers and Datasets What is LASSL • How to Use What is LASSL LASSL은 LAnguage Semi-Super
Pangu-Alpha for Transformers
Pangu-Alpha for Transformers Usage Download MindSpore FP32 weights for GPU from here to data/Pangu-alpha_2.6B.ckpt Activate MindSpore environment and
easyNeuron is a simple way to create powerful machine learning models, analyze data and research cutting-edge AI.
easyNeuron is a simple way to create powerful machine learning models, analyze data and research cutting-edge AI.
A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal
A lightweight library designed to accelerate the process of training PyTorch models by providing a minimal, but extensible training loop which is flexible enough to handle the majority of use cases, and capable of utilizing different hardware options with no code changes required.
[NeurIPS 2021]: Are Transformers More Robust Than CNNs? (Pytorch implementation & checkpoints)
Are Transformers More Robust Than CNNs? Pytorch implementation for NeurIPS 2021 Paper: Are Transformers More Robust Than CNNs? Our implementation is b
Official code for "Maximum Likelihood Training of Score-Based Diffusion Models", NeurIPS 2021 (spotlight)
Maximum Likelihood Training of Score-Based Diffusion Models This repo contains the official implementation for the paper Maximum Likelihood Training o
Autoregressive Models in PyTorch.
Autoregressive This repository contains all the necessary PyTorch code, tailored to my presentation, to train and generate data from WaveNet-like auto
Models, datasets and tools for Facial keypoints detection
Template for Data Science Project This repo aims to give a robust starting point to any Data Science related project. It contains readymade tools setu
Explainability of the Implications of Supervised and Unsupervised Face Image Quality Estimations Through Activation Map Variation Analyses in Face Recognition Models
Explainable_FIQA_WITH_AMVA Note This is the official repository of the paper: Explainability of the Implications of Supervised and Unsupervised Face I
2D Human Pose estimation using transformers. Implementation in Pytorch
PE-former: Pose Estimation Transformer Vision transformer architectures perform very well for image classification tasks. Efforts to solve more challe
Music Source Separation; Train & Eval & Inference piplines and pretrained models we used for 2021 ISMIR MDX Challenge.
Introduction 1. Usage (For MSS) 1.1 Prepare running environment 1.2 Use pretrained model 1.3 Train new MSS models from scratch 1.3.1 How to train 1.3.
Experiments and examples converting Transformers to ONNX
Experiments and examples converting Transformers to ONNX This repository containes experiments and examples on converting different Transformers to ON
Visual Adversarial Imitation Learning using Variational Models (VMAIL)
Visual Adversarial Imitation Learning using Variational Models (VMAIL) This is the official implementation of the NeurIPS 2021 paper. Project website
A collection of models, views, middlewares, and forms to help secure a Django project.
Django-Security This package offers a number of models, views, middlewares and forms to facilitate security hardening of Django applications. Full doc
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (Paper) (Slides) (Video) RuleBERT is a pre-trained language model that has been fine-tune
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
Seldon Core: Blazing Fast, Industry-Ready ML An open source platform to deploy your machine learning models on Kubernetes at massive scale. Overview S
Uni-Fold: Training your own deep protein-folding models.
Uni-Fold: Training your own deep protein-folding models. This package provides and implementation of a trainable, Transformer-based deep protein foldi
Torchrecipes provides a set of reproduci-able, re-usable, ready-to-run RECIPES for training different types of models, across multiple domains, on PyTorch Lightning.
Recipes are a standard, well supported set of blueprints for machine learning engineers to rapidly train models using the latest research techniques without significant engineering overhead.Specifically, recipes aims to provide- Consistent access to pre-trained SOTA models ready for production- Reference implementations for SOTA research reproducibility, and infrastructure to guarantee correctness, efficiency, and interoperability.
State-of-the-art NLP through transformer models in a modular design and consistent APIs.
Trapper (Transformers wRAPPER) Trapper is an NLP library that aims to make it easier to train transformer based models on downstream tasks. It wraps h
EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation.
This repository contains data and code for our EMNLP 2021 paper Models and Datasets for Cross-Lingual Summarisation. Please contact me at [email protected]
A modular application for performing anomaly detection in networks
Deep-Learning-Models-for-Network-Annomaly-Detection The modular app consists for mainly three annomaly detection algorithms. The system supports model
Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.
Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.
Rapid experimentation and scaling of deep learning models on molecular and crystal graphs.
LitMatter A template for rapid experimentation and scaling deep learning models on molecular and crystal graphs. How to use Clone this repository and
Hashformers is a framework for hashtag segmentation with transformers.
Hashtag segmentation is the task of automatically inserting the missing spaces between the words in a hashtag. Hashformers applies Transformer models
Markov Attention Models
Introduction This repo contains code for reproducing the results in the paper Graphical Models with Attention for Context-Specific Independence and an
Implementation of paper "Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal"
Patch-wise Adversarial Removal Implementation of paper "Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal
SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning
SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning This repository is the official implementation of "SHRIMP: Sparser Random Featur
S-attack library. Official implementation of two papers "Are socially-aware trajectory prediction models really socially-aware?" and "Vehicle trajectory prediction works, but not everywhere".
S-attack library: A library for evaluating trajectory prediction models This library contains two research projects to assess the trajectory predictio
Post-Training Quantization for Vision transformers.
PTQ4ViT Post-Training Quantization Framework for Vision Transformers. We use the twin uniform quantization method to reduce the quantization error on