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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/ShadenSmith/ghpages-test
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/ShannonAI/Entity-Relation-As-Multi-Turn-QA
https://github.com/ShaoaAllen/CogQA
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
https://github.com/Sharif-SLPL/t5-fa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Sharpiless/Versailles-text-generation-with-paddlepaddle
https://github.com/ShashwatNigam99/MARBLER
Emergence of Grounded Compositional Language in Multi-Agent Populations
https://github.com/Shasvat-Desai/Shasvat-Desai
https://github.com/ShayekhBinIslam/openrag
https://github.com/ShichaoSun/math_seq2tree
https://github.com/Shinya-Kouda/kgc
https://github.com/Shito0907/3D_resnet
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/ShivamRajSharma/Transformer-Architectures-From-Scratch
https://github.com/Shravsridhar/NLP-Question-Answer-Model
https://github.com/Shravsridhar/QA-Model-INFO-7374
https://github.com/ShuvenduBikash/transformer_spelling_corrector
https://github.com/ShwetaBaranwal/Transformer
https://github.com/Sikun/dlam_project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/SimengSun/CIS530-project
SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation
https://github.com/SindhuMadi/FakeNewsDetection
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/SkullFang/BERT_NLP_Classification
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Skumarr53/Attention-is-All-you-Need-PyTorch
https://github.com/SmartDataAnalytics/Polisis_Benchmark
Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning
https://github.com/Smerity/keras_snli
https://github.com/Smerity/pytorch-lamb
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/Soikonomou/albert_final
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/Soikonomou/albert_final_infer12
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/Soikonomou/albert_final_infer8
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/Soikonomou/bert_new
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/Soikonomou/bert_new_new
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/Songlielie/DKT-Plus
Adding Gradient Noise Improves Learning for Very Deep Networks
https://github.com/Soren-Kierkegaard/BERT-for-tweet-Classifcation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/SpikeKing/My-Bert
https://github.com/Sreyan88/GAMA
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities
https://github.com/SteveTANTAN/PoG
Paths-over-Graph: Knowledge Graph Empowered Large Language Model Reasoning
https://github.com/StevenLai1994/AM_and_LM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.