Med-VQA In this repository we have tested 3 VQA models on the ImageCLEF-2019 dataset. Two of these are made on top of Facebook AI Reasearch's Multi-Mo

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OpenNRE We have a DEMO website (http://opennre.thunlp.ai/). Try it out! OpenNRE is an open-source and extensible toolkit that provides a unified frame

Open-Domain Question Answering(ODQA)는 다양한 주제에 대한 문서 집합으로부터 자연어 질의에 대한 답변을 찾아오는 task입니다. 이때 사용자 질의에 답변하기 위해 주어지는 지문이 따로 존재하지 않습니다. 따라서 사전에 구축되어있는 Knowl

Automatic summarization is the process of shortening a text document with software, in order to create a summary with the major points of the original document. Technologies that can make a coherent

This repository is an implementation of Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis (SV2TTS) with a vocoder that works in real-time. Feel free to check my the

NVDA NVDA (NonVisual Desktop Access) is a free, open source screen reader for Microsoft Windows. It is developed by NV Access in collaboration with a

squote A semantic search engine that takes some input text and returns some (questionably) relevant (questionably) famous quotes. Built with: bert-as-

MicBot MicBot uses Google Translate to speak everyone's chat messages. It can al

Improving Transformer Models by Reordering their Sublayers This repository contains the code for running the character-level Sandwich Transformers fro

eBook Reader Dictionaries Finally, decent dictionaries based on Wiktionary for your beloved eBook reader. Dictionaries Catalan 🚧 Ελληνικά (help welco

한국어 문서 koSpellPy Korean Spell checker How to use Install pip install kospellpy Use from kospellpy import spell_init spell_checker = spell_init() # d

Voice-conversion-evaluation An evaluation toolkit for voice conversion models. Sample test pair Generate the metadata for evaluating models. The direc

MedMCQA MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering A large-scale, Multiple-Choice Question Answe

Speech Separation The simple project to separate mixed voice (2 clean voices) to 2 separate voices. Result Example (Clisk to hear the voices): mix ||

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医学实体抽取_GlobalPointer_torch 介绍 思想来自于苏神 GlobalPointer，原始版本是基于keras实现的，模型结构实现参考现有 pytorch 复现代码【感谢!】，基于torch百分百复现苏神原始效果。 数据集 中文医学命名实体数据集 点这里申请，很简单，共包含九类医学

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

This repository contains the code for running modular summarization pipelines as described in the publication Krishna K, Khosla K, Bigham J, Lipton ZC

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