Papers with Code 아카이브
Question Answering
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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/sherinaseri/bert
https://github.com/sheryl-ai/MemGCN
https://github.com/shi27feng/transformers.satisfy
https://github.com/shibing624/medicalgpt
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
https://github.com/shihaozhaozsh/lavi-bridge
https://github.com/shikhar1sharma/NLP-Resources
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/shimisalant/RaSoR
Learning Recurrent Span Representations for Extractive Question Answering
https://github.com/shin-ee-chen/bla
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
https://github.com/shining-spring/nvlda
https://github.com/shishir-roy/decaNLP-with-BERT
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/shivamraval98/multitask-t5_ae
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/shivanidogra0301/NLP-Deep_Learning_Chatbot
https://github.com/shmsw25/AmbigQA
https://github.com/shmsw25/GraphRetriever
Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
https://github.com/shmsw25/bart-closed-book-qa
https://github.com/shmsw25/factscore
https://github.com/shmsw25/qa-transfer
https://github.com/shmulvad/zero-for-ner
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/shouvikcirca/Transformer
https://github.com/shouvikcirca/Transformer_MiniProject
https://github.com/showlab/show-o
Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
https://github.com/shreyash8/English_to_Hindi_NMT
https://github.com/shreyashankar/gpt3-sandbox
https://github.com/shuaishuaij/Machine-Translation
https://github.com/shubham-gupta-iitr/mmmlX
https://github.com/shyam-skk/Parallel_CNN_Transfromer
https://github.com/sid-sundrani/differentiable-kb-qa
Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection
https://github.com/siddk/entity-network
https://github.com/siddk/relation-network
https://github.com/sierra-research/tau-bench
https://github.com/sigtyp/ST2024
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/simon-benigeri/narrative-generation
https://github.com/simonjisu/E2EMN
https://github.com/simonjisu/annotated-transformer-kr
https://github.com/simran-arora/focus
Ask Me Anything: A simple strategy for prompting language models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.