https://github.com/ChernovAndrey/chronos-forecasting-wasserstein
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Papers with Code 아카이브
Question Generation
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The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/GauthierDmn/question_generation
Learning to Ask: Neural Question Generation for Reading Comprehension
https://github.com/IzabelaKrupinska/PROJBAD
Deep Visual-Semantic Alignments for Generating Image Descriptions
https://github.com/JunnYu/x-transformers-paddle
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KAGUYAHONGLAI/SRC
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Ki6an/fastT5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KnightZhang625/BERT_TF
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/LLaVA-VL/LLaVA-NeXT
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
https://github.com/LeoLaugier/conditional-auto-encoder-text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/LeonZh0u/Chatbot
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/LiamMaclean216/Pytorch-Transfomer
https://github.com/Lieberk/Paddle-AoA-Captioning
Deep Visual-Semantic Alignments for Generating Image Descriptions
https://github.com/MS-P3/code5/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/MS-P3/code7/tree/main/xlm_prophetnet
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
https://github.com/MindCode-4/code-10/tree/main/FDDE/train-fine
https://github.com/MindCode-4/code-5/tree/main/xlm_prophetnet
https://github.com/MindSpore-scientific-2/code-11/tree/main/Quiz_gen
https://github.com/MindSpore-scientific-2/code-4/tree/main/Quiz_gen
Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation
https://github.com/MindSpore-scientific/code-12/tree/main/FDDE/train-fine
https://github.com/MinyeLee/Question-Generation-Pytorch
Learning to Ask: Neural Question Generation for Reading Comprehension
https://github.com/MorenoLaQuatra/bart-it
IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
https://github.com/MrFeelgoood/RealEstateStocksForecasting
IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
https://github.com/Nimesh-Patel/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/PaddlePaddle/PaddleNLP/blob/develop/paddlenlp/transformers/ernie_gen/modeling.py
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Prakhar0409/question-generation
Learning to Ask: Neural Question Generation for Reading Comprehension
https://github.com/Sharif-SLPL/t5-fa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Sharpiless/Versailles-text-generation-with-paddlepaddle
https://github.com/StatNLP/ada4asr
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/SunJiamei/LRP-imagecaptioning-pytorch
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/VinitSR7/Image-Caption-Generation
Deep Visual-Semantic Alignments for Generating Image Descriptions
https://github.com/YunwenTechnology/Unilm
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/YuxiXie/Neural-Question-Generation
https://github.com/aaronbae/AnaQA
Multi-hop Reading Comprehension through Question Decomposition and Rescoring
https://github.com/abelriboulot/onnxt5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/abhigoyal1997/CS-763-Project
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.