Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 19
- 평가표
- 153
- 보존된 평가 기록
- 1,781
Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JRC1995/Machine-Translation-Transformers
https://github.com/Jacfger/simple-stuffs
https://github.com/Jackma3/NER-Bert-BiLstm
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JamesJang26/YALENLP-PerAnsSumm-2025
https://github.com/JanAthmer/Compositional-generalization-capabillity-of-Transformer
https://github.com/Jason-J-Choi/DeBERTa_TxtClassifier
https://github.com/JeheonPark596/sentimentsurvival
https://github.com/JeremyZhao1998/Nystromformer-Paddle
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention
https://github.com/Jeremyyny/Value-Spectrum
https://github.com/JiachengLi1995/UCTopic
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/Jmkernes/PAR-Transformer-XL
https://github.com/JoegameZhou/mPanGu-Alpha-53
https://github.com/JohannLee1996/bert
https://github.com/Joshuaoneheart/Fintech-Final-Report
https://github.com/Jun-Zhang-32108/Sentiment-Analysis
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/JunnYu/x-transformers-paddle
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/JunweiLiang/DualAttentionNetwork
Dual Attention Networks for Multimodal Reasoning and Matching
https://github.com/JunweiLiang/FVTA_memoryqa
https://github.com/KAGUYAHONGLAI/SRC
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KNOT-FIT-BUT/scalingQA
Pruning the Index Contents for Memory Efficient Open-Domain QA
https://github.com/Kaimaoge/IGNNK
https://github.com/Kanishkparganiha/Named-Entity-Recognition-using-BERT-with-PyTorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Karthik-Bhaskar/Context-Based-Question-Answering
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/KastanDay/video-pretrained-transformer
https://github.com/Katsumata420/bert_mlm
https://github.com/KaveeshaSIlva/Baseline-FYP
PathVQA: 30000+ Questions for Medical Visual Question Answering
https://github.com/KaveeshaSIlva/PathVQA
PathVQA: 30000+ Questions for Medical Visual Question Answering
https://github.com/KeithGalli/pycon2020
https://github.com/Kerram/Deephol-Bert-Zpp
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Ketansuhaas/scope-diffusers
Progressive Prompt Detailing for Improved Alignment in Text-to-Image Generative Models
https://github.com/Kevin-Vora/bert-embedding-gluonnlp-edit-
https://github.com/KevinPang180/3D-ResNets-PaddlePaddle
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/Khalid-Jamal/annotated-transformer-Arabic
https://github.com/Ki6an/fastT5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Kirankumaraswamy/Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.