https://github.com/DevSinghSachan/art
End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering
Papers with Code 아카이브
Answer Generation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Hanlard/Bert-for-WebQA
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
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/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/MS-P3/code5/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/MindSpore-scientific-2/code-11/tree/main/Quiz_gen
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/OpenNLPhub/WebQA-Using-DGCNN
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
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/Sharif-SLPL/t5-fa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/VietHoang1512/khmer-nltk
https://github.com/VietHoang1710/khmer-nltk
https://github.com/WangJiuniu/SRQA
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
https://github.com/abelriboulot/onnxt5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/agrzheng/GRADA
PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models
https://github.com/airc-keti/ke-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/c4-documentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/allenai/dolma
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/amazon-science/chronos-forecasting
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/asahi417/lmppl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/bigscience-workshop/architecture-objective
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/cccntu/ft5-demo-space
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/codexxxl/GraphVQA
https://github.com/conceptofmind/LaMDA-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/conceptofmind/lamda-rlhf-pytorch
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/dawn0815/UniSA
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/facebookresearch/atlas
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/t5x
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/t5x_retrieval
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google-research/text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/google/seqio
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/gulucaptain/dynamictrl
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.