Papers with Code 아카이브
Question Answering
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- 평가표
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- 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/KnightZhang625/BERT_TF
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/KomorebiLHX/Emotion-Recognition-in-Conversations
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/KoshiroSato/Flask_NLP_App
mT5: A massively multilingual pre-trained text-to-text transformer
https://github.com/KoshiroSato/Simple_Transformers_mT5_finetuning
mT5: A massively multilingual pre-trained text-to-text transformer
https://github.com/Kosuke-Szk/BERT-NER-ja
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/KrishnaTarun/Deep-Learning-Lab
https://github.com/Kristof-Neys/GAT_Neo4j_DGL
https://github.com/KushGrandhi/Polaroid
https://github.com/Kyubyong/bert-token-embeddings
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Kyubyong/transformer
https://github.com/LaiXinyi823/emotect
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/LamLauChiu/Tensorflow_Learning
https://github.com/Lee-Ft/RHA
Relation-aware Hierarchical Attention Framework for Video Question Answering
https://github.com/Leeroo-AI/mergoo
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM
https://github.com/Lemoners/Attention-Is-All-You-Need
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/LeoWood/bert-horovod
https://github.com/LeonZh0u/Chatbot
Unified Language Model Pre-training for Natural Language Understanding and Generation
https://github.com/Leputa/CIKM-AnalytiCup-2018
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
https://github.com/LiamMaclean216/Pytorch-Chatbot
https://github.com/LiamMaclean216/Pytorch-Transfomer
https://github.com/Liangtaiwan/self-training-mcqa
Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge
https://github.com/Lightning-AI/lit-gpt
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
https://github.com/Lightning-AI/lit-llama
https://github.com/Linar23/Research_work
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/LorenzoAgnolucci/BERT_for_ABSA
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Lou1sM/AML-Project
https://github.com/Lou1sM/AdvancedML-Project-Dynamic-Coattention-Networks
https://github.com/LoveYang/bert_test
https://github.com/Lsdefine/attention-is-all-you-need-keras
https://github.com/LucaMaurici/MultiHop-Reading-Comprehension-DRL
Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal
https://github.com/Lucas2012/ProbabilisticNeuralProgrammedNetwork
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
https://github.com/Lukasz-G/Hydra
https://github.com/MCG-NJU/VideoChat-Online
https://github.com/MCLYang/RhythmGAN_pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.