A telegram bot to translate 100+ Languages
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Aɴᴋɪᴛ Kᴜᴍᴀʀ
This is an incredibly powerful calculator that is capable of many useful day-to-day functions.
Description 💻 This is an incredibly powerful calculator that is capable of many useful day-to-day functions. Such functions include solving basic ari
Official implementation of Meta-StyleSpeech and StyleSpeech
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation Dongchan Min, Dong Bok Lee, Eunho Yang, and Sung Ju Hwang This is an official code
💫 Industrial-strength Natural Language Processing (NLP) in Python
spaCy: Industrial-strength NLP spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest researc
Codes for coreference-aware machine reading comprehension
Data and code for the paper "Tracing Origins: Coreference-aware Machine Reading Comprehension" at ACL2022. Dataset There are three folders for our thr
Arabic-Phonetic-Output - You can input the phonetic version of any Arabic text here. This software will show you output in Arabic (with vowels)
Arabic-Phonetic-Output You can input the phonetic version of any Arabic text her
AMUSE - financial summarization
AMUSE AMUSE - financial summarization Unzip data.zip Train new model: python FinAnalyze.py --task train --start 0 --count how many files,-1 for all
This repository will contain the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"
GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields Project Page | Paper | Supplementary | Video | Slides | Blog | Talk If
Universal End2End Training Platform, including pre-training, classification tasks, machine translation, and etc.
背景 安装教程 快速上手 (一)预训练模型 (二)机器翻译 (三)文本分类 TenTrans 进阶 1. 多语言机器翻译 2. 跨语言预训练 背景 TrenTrans是一个统一的端到端的多语言多任务预训练平台,支持多种预训练方式,以及序列生成和自然语言理解任务。 安装教程 git clone git
My Implementation for the paper EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks using Tensorflow
Easy Data Augmentation Implementation This repository contains my Implementation for the paper EDA: Easy Data Augmentation Techniques for Boosting Per
voice2json is a collection of command-line tools for offline speech/intent recognition on Linux
Command-line tools for speech and intent recognition on Linux
A framework for evaluating Knowledge Graph Embedding Models in a fine-grained manner.
A framework for evaluating Knowledge Graph Embedding Models in a fine-grained manner.
Awesome-NLP-Research (ANLP)
Awesome-NLP-Research (ANLP)
PyTorch implementation of the paper: Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding This repository contains the official PyTorch implementation of th
SHAS: Approaching optimal Segmentation for End-to-End Speech Translation
SHAS: Approaching optimal Segmentation for End-to-End Speech Translation In this repo you can find the code of the Supervised Hybrid Audio Segmentatio
PortaSpeech - PyTorch Implementation
PortaSpeech - PyTorch Implementation PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech. Model Size Module Nor
Residual2Vec: Debiasing graph embedding using random graphs
Residual2Vec: Debiasing graph embedding using random graphs This repository contains the code for S. Kojaku, J. Yoon, I. Constantino, and Y.-Y. Ahn, R
Deep Learning for Natural Language Processing - Lectures 2021
This repository contains slides for the course "20-00-0947: Deep Learning for Natural Language Processing" (Technical University of Darmstadt, Summer term 2021).
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
Watson Natural Language Understanding and Knowledge Studio
Material de demonstração dos serviços: Watson Natural Language Understanding e Knowledge Studio Visão Geral: https://www.ibm.com/br-pt/cloud/watson-na
gaiic2021-track3-小布助手对话短文本语义匹配复赛rank3、决赛rank4
决赛答辩已经过去一段时间了,我们队伍ac milan最终获得了复赛第3,决赛第4的成绩。在此首先感谢一些队友的carry~ 经过2个多月的比赛,学习收获了很多,也认识了很多大佬,在这里记录一下自己的参赛体验和学习收获。