Papers with Code 아카이브
Question Answering
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- 하위 태스크
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- 평가표
- 153
- 보존된 평가 기록
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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/StoneGH/bert
https://github.com/StuartCHAN/KARL
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/SumbeeLei/Math_EN
https://github.com/SumitM0432/Quora-Insincere-Questions-Classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Sunnyzhr/habitat-lab
https://github.com/SuperpoweredAI/spRAG
FinanceBench: A New Benchmark for Financial Question Answering
https://github.com/Suro-One/Hyena-Hierarchy
Hyena Hierarchy: Towards Larger Convolutional Language Models
https://github.com/Susheel-1999/Sentence_Similarity
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Swati17293/HQS
Hierarchical Deep Multi-modal Network for Medical Visual Question Answering
https://github.com/Sypherd/lm-evaluation-harness
https://github.com/TDeepanshPandey/Chat_Bot
https://github.com/TEAMLAB-Lecture/deep_nlp_101
https://github.com/THUDM/ProteinLM
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/THUNLP-MT/THUMT
https://github.com/TJUIRLAB/CIKM2018_QMWFLM
A Quantum Many-body Wave Function Inspired Language Modeling Approach
https://github.com/TOSHISTATS/Classification-of-Japanese-news-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/TRKuan/cxr8
https://github.com/TSLNIHAOGIT/QANet_keras_debug
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/TSLNIHAOGIT/bert
https://github.com/TSLNIHAOGIT/bert_run
https://github.com/TYTYTYTYTYTYTYTYTY/558-project
https://github.com/TYtrack/FedNLP
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks
https://github.com/TannerFry/CS522-Final-Project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Tao-Mind/QDD_Net
https://github.com/TaoQin/taoqin.github.io
BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining
https://github.com/Tashnim-Chowdhury/FloodNet-Challenge-EARTHVISION2021
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
https://github.com/TeamLab/bert-gcn-for-paper-citation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Tencent/TurboTransformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Tetsuya-Nishikawa/ConvLSTM_DEMO
https://github.com/ThanThoai/Visual-Question-Answering_Vietnamese
Deep Modular Co-Attention Networks for Visual Question Answering
https://github.com/The-AI-Summer/self_attention
https://github.com/TheAiSingularity/graphrag-local-ollama
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
https://github.com/TheCyberian/androidMalwareDetectionWithNLP
https://github.com/TheCyberian/windowsMalwareDetectionWithNLP
https://github.com/TheInfamousWayne/CheXNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.