Large-scale open domain KNOwledge grounded conVERsation system based on PaddlePaddle

Related tags

Deep LearningKnover
Overview

Knover

Knover is a toolkit for knowledge grounded dialogue generation based on PaddlePaddle. Knover allows researchers and developers to carry out efficient training/inference of large-scale dialogue generation models.

What's New:

  • February 2021: We are opening our implementation (Team 19) in DSTC9-Track1.
  • July 2020: We are opening PLATO-2, a large-scale generative model with latent space for open-domain dialogue systems.

Requirements and Installation

  • python version >= 3.7
  • paddlepaddle-gpu version >= 2.0.0
  • sentencepiece
  • termcolor
  • If you want to run distributed training, you'll also need NCCL
  • Install Knover locally:
git clone https://github.com/PaddlePaddle/Knover.git
cd Knover
pip3 install -e .
  • Or you can setup PYTHONPATH only:
export PYTHONPATH=/abs/path/to/Knover:$PYTHONPATH

Basic usage

Disclaimer

This project aims to facilitate further research progress in dialogue generation. Baidu is not responsible for the 3rd party's generation with the pre-trained system.

Contact information

For help or issues using Knover, please submit a GitHub issue.

Team Enigma at ArgMining 2021 Shared Task: Leveraging Pretrained Language Models for Key Point Matching

Team Enigma at ArgMining 2021 Shared Task: Leveraging Pretrained Language Models for Key Point Matching This is our attempt of the shared task on Quan

Manav Nitin Kapadnis 12 Jul 08, 2022
Official Implementation for "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" (ICCV 2021 Oral)

StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery (ICCV 2021 Oral) Run this model on Replicate Optimization: Global directions: Mapper: Check ou

3.3k Jan 05, 2023
Freecodecamp Scientific Computing with Python Certification; Solution for Challenge 2: Time Calculator

Assignment Write a function named add_time that takes in two required parameters and one optional parameter: a start time in the 12-hour clock format

Hellen Namulinda 0 Feb 26, 2022
Code of 3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces

3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces Installation After cloning the repo open

37 Dec 03, 2022
[WACV 2022] Contextual Gradient Scaling for Few-Shot Learning

CxGrad - Official PyTorch Implementation Contextual Gradient Scaling for Few-Shot Learning Sanghyuk Lee, Seunghyun Lee, and Byung Cheol Song In WACV 2

Sanghyuk Lee 4 Dec 05, 2022
NAACL2021 - COIL Contextualized Lexical Retriever

COIL Repo for our NAACL paper, COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List. The code covers learning

Luyu Gao 108 Dec 31, 2022
Yoloxkeypointsegment - An anchor-free version of YOLO, with a simpler design but better performance

Introduction 关键点版本:已完成 全景分割版本:已完成 实例分割版本:已完成 YOLOX is an anchor-free version of

23 Oct 20, 2022
Converting CPT to bert form for use

cpt-encoder 将CPT转成bert形式使用 说明 刚刚刷到又出了一种模型:CPT,看论文显示,在很多中文任务上性能比mac bert还好,就迫不及待想把它用起来。 根据对源码的研究,发现该模型在做nlu建模时主要用的encoder部分,也就是bert,因此我将这部分权重转为bert权重类型

黄辉 1 Oct 14, 2021
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

Ruan Chaves 41 Nov 09, 2022
Tensorflow AffordanceNet and AffContext implementations

AffordanceNet and AffContext This is tensorflow AffordanceNet and AffContext implementations. Both are implemented and tested with tensorflow 2.3. The

Beatriz Pérez 6 Dec 01, 2022
Self-supervised Label Augmentation via Input Transformations (ICML 2020)

Self-supervised Label Augmentation via Input Transformations Authors: Hankook Lee, Sung Ju Hwang, Jinwoo Shin (KAIST) Accepted to ICML 2020 Install de

hankook 96 Dec 29, 2022
Crowd-sourced Annotation of Human Motion.

Motion Annotation Tool Live: https://motion-annotation.humanoids.kit.edu Paper: The KIT Motion-Language Dataset Installation Start by installing all P

Matthias Plappert 4 May 25, 2020
Temporally Efficient Vision Transformer for Video Instance Segmentation, CVPR 2022, Oral

Temporally Efficient Vision Transformer for Video Instance Segmentation Temporally Efficient Vision Transformer for Video Instance Segmentation (CVPR

Hust Visual Learning Team 203 Dec 31, 2022
Implementation of ICCV 2021 oral paper -- A Novel Self-Supervised Learning for Gaussian Mixture Model

SS-GMM Implementation of ICCV 2021 oral paper -- Self-Supervised Image Prior Learning with GMM from a Single Noisy Image with supplementary material R

HUST-The Tan Lab 4 Dec 05, 2022
Meshed-Memory Transformer for Image Captioning. CVPR 2020

M²: Meshed-Memory Transformer This repository contains the reference code for the paper Meshed-Memory Transformer for Image Captioning (CVPR 2020). Pl

AImageLab 422 Dec 28, 2022
Dynamic Environments with Deformable Objects (DEDO)

DEDO - Dynamic Environments with Deformable Objects DEDO is a lightweight and customizable suite of environments with deformable objects. It is aimed

Rika 32 Dec 22, 2022
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

TorchCAM: class activation explorer Simple way to leverage the class-specific activation of convolutional layers in PyTorch. Quick Tour Setting your C

F-G Fernandez 1.2k Dec 29, 2022
Another pytorch implementation of FCN (Fully Convolutional Networks)

FCN-pytorch-easiest Trying to be the easiest FCN pytorch implementation and just in a get and use fashion Here I use a handbag semantic segmentation f

Y. Dong 158 Dec 21, 2022
Official implementation of "Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets" (CVPR2021)

Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets This is the official implementation of "Towards Good Pract

Sanja Fidler's Lab 52 Nov 22, 2022
EfficientNetV2 implementation using PyTorch

EfficientNetV2-S implementation using PyTorch Train Steps Configure imagenet path by changing data_dir in train.py python main.py --benchmark for mode

Jahongir Yunusov 86 Dec 29, 2022