Nlp chinese corpus大规模中文自然语言处理语料 Large Scale Chinese Corpus for NLP
Stars: ✭ 6,656 (+51100%)
DocQNAuthor implementation of "Learning to Search in Long Documents Using Document Structure" (Mor Geva and Jonathan Berant, 2018)
Stars: ✭ 21 (+61.54%)
MICCAI21 MMQMultiple Meta-model Quantifying for Medical Visual Question Answering
Stars: ✭ 16 (+23.08%)
ACVR2017An Innovative Salient Object Detection Using Center-Dark Channel Prior
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sinonimo🇧🇷 Sinonimo é um pacote Node que traz sinônimos de palavras em português
Stars: ✭ 14 (+7.69%)
cdQA-ui⛔ [NOT MAINTAINED] A web interface for cdQA and other question answering systems.
Stars: ✭ 19 (+46.15%)
mrqaCode for EMNLP-IJCNLP 2019 MRQA Workshop Paper: "Domain-agnostic Question-Answering with Adversarial Training"
Stars: ✭ 35 (+169.23%)
Giveme5WExtraction of the five journalistic W-questions (5W) from news articles
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cherche📑 Neural Search
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WikiTableQuestionsA dataset of complex questions on semi-structured Wikipedia tables
Stars: ✭ 81 (+523.08%)
productqaProduct-Aware Answer Generation in E-Commerce Question-Answering
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unanswerable qaThe official implementation for ACL 2021 "Challenges in Information Seeking QA: Unanswerable Questions and Paragraph Retrieval".
Stars: ✭ 21 (+61.54%)
snorkelingExtracting biomedical relationships from literature with Snorkel 🏊
Stars: ✭ 56 (+330.77%)
PororoQAPororoQA, https://arxiv.org/abs/1707.00836
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recurrent-defocus-deblurring-synth-dual-pixelReference github repository for the paper "Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data". We propose a procedure to generate realistic DP data synthetically. Our synthesis approach mimics the optical image formation found on DP sensors and can be applied to virtual scenes rendered with standard computer software. Lev…
Stars: ✭ 30 (+130.77%)
word2vec-pt-brImplementação e modelo gerado com o treinamento (trigram) da wikipedia em pt-br
Stars: ✭ 34 (+161.54%)
qaTensorFlow Models for the Stanford Question Answering Dataset
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QA4IEOriginal implementation of QA4IE
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elm-pt-brElm lang "awesome list" em pt-BR
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query-focused-sumOfficial code repository for "Exploring Neural Models for Query-Focused Summarization".
Stars: ✭ 17 (+30.77%)
climateRAn R 📦 for getting point and gridded climate data by AOI
Stars: ✭ 93 (+615.38%)
TOEFL-QAA question answering dataset for machine comprehension of spoken content
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MSMARCOMachine Comprehension Train on MSMARCO with S-NET Extraction Modification
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ComoFazerUmaPerguntaPT🤔 Farto de fazer perguntas e não ser respondido? Aprenda agora a melhor forma de fazer uma pergunta 🔥
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StargraphStarGraph (aka *graph) is a graph database to query large Knowledge Graphs. Playing with Knowledge Graphs can be useful if you are developing AI applications or doing data analysis over complex domains.
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BIRLBIRL: Benchmark on Image Registration methods with Landmark validations
Stars: ✭ 66 (+407.69%)
squadgymEnvironment that can be used to evaluate reasoning capabilities of artificial agents
Stars: ✭ 27 (+107.69%)
number-to-wordsconvert number into words (english, french, italian, roman, spanish, portuguese, belgium, dutch, swedish, polish, russian, iranian, roman, aegean)
Stars: ✭ 53 (+307.69%)
Medi-CoQAConversational Question Answering on Clinical Text
Stars: ✭ 22 (+69.23%)
HARRecognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.
Stars: ✭ 18 (+38.46%)
head-qaHEAD-QA: A Healthcare Dataset for Complex Reasoning
Stars: ✭ 20 (+53.85%)
mcQA🔮 Answering multiple choice questions with Language Models.
Stars: ✭ 23 (+76.92%)
KrantikariQAAn InformationGain based Question Answering over knowledge Graph system.
Stars: ✭ 54 (+315.38%)
pump-and-dump-datasetAdditional material for paper: Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations, ICCCN '20
Stars: ✭ 66 (+407.69%)
co-attentionPytorch implementation of "Dynamic Coattention Networks For Question Answering"
Stars: ✭ 54 (+315.38%)
deformer[ACL 2020] DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering
Stars: ✭ 111 (+753.85%)
Resumos EMAP-FGVRepositório de resumos do curso de Matemática Aplicada da FGV-EMAP
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PersianQAPersian (Farsi) Question Answering Dataset (+ Models)
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tg2021taskParticipant Kit for the TextGraphs-15 Shared Task on Explanation Regeneration
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denspiReal-Time Open-Domain Question Answering with Dense-Sparse Phrase Index (DenSPI)
Stars: ✭ 188 (+1346.15%)
dropzone-ui-reactThe most complete React Library Component for drag’n’drop files. Image and video previews. File validation. Multilanguage. Server side support.
Stars: ✭ 122 (+838.46%)
GARCode and resources for papers "Generation-Augmented Retrieval for Open-Domain Question Answering" and "Reader-Guided Passage Reranking for Open-Domain Question Answering", ACL 2021
Stars: ✭ 38 (+192.31%)
DVQA datasetDVQA Dataset: A Bar chart question answering dataset presented at CVPR 2018
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TVQAplus[ACL 2020] PyTorch code for TVQA+: Spatio-Temporal Grounding for Video Question Answering
Stars: ✭ 99 (+661.54%)
iPerceiveApplying Common-Sense Reasoning to Multi-Modal Dense Video Captioning and Video Question Answering | Python3 | PyTorch | CNNs | Causality | Reasoning | LSTMs | Transformers | Multi-Head Self Attention | Published in IEEE Winter Conference on Applications of Computer Vision (WACV) 2021
Stars: ✭ 52 (+300%)
WikiQAVery Simple Question Answer System using Chinese Wikipedia Data
Stars: ✭ 24 (+84.62%)
patrick-wechat⭐️🐟 questionnaire wechat app built with taro, taro-ui and heart. 微信问卷小程序
Stars: ✭ 74 (+469.23%)
MLH-QuizzetThis is a smart Quiz Generator that generates a dynamic quiz from any uploaded text/PDF document using NLP. This can be used for self-analysis, question paper generation, and evaluation, thus reducing human effort.
Stars: ✭ 23 (+76.92%)
text2textText2Text: Cross-lingual natural language processing and generation toolkit
Stars: ✭ 188 (+1346.15%)
icebreakerWeb app that allows students to ask real-time, anonymous questions during class
Stars: ✭ 16 (+23.08%)
Audio-Classification-using-CNN-MLPMulti class audio classification using Deep Learning (MLP, CNN): The objective of this project is to build a multi class classifier to identify sound of a bee, cricket or noise.
Stars: ✭ 36 (+176.92%)