https://github.com/TheNeuromancer/SentEmb
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Papers with Code 아카이브
Question Answering
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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/TheShadow29/visual-commonsense-pytorch
https://github.com/TheTensorDude/transformer-tf
https://github.com/ThilinaRajapakse/simpletransformers
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/Thomasyyj/EEEQA
EEE-QA: Exploring Effective and Efficient Question-Answer Representations
https://github.com/TidalPaladin/neural-summarizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/TieDanCuihua/transformer-Attention-is-All-You-Need
https://github.com/TommyWongww/MemoryNetworks
https://github.com/TonyX19/bert_hyperpartisan
https://github.com/TraffiX-VideoQA/TUMTraffic-VideoQA-Baseline
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
https://github.com/Tsinghua-MARS-Lab/M2I
M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction
https://github.com/TyngJiunKuo/deep-learning-project
https://github.com/UKPLab/coling2018-graph-neural-networks-question-answering
Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
https://github.com/UKPLab/elmo-bilstm-cnn-crf
https://github.com/UdbhavPrasad072300/Transformer-Implementation
https://github.com/UdbhavPrasad072300/Transformer-Implementation-and-Language-Translation
https://github.com/UdbhavPrasad072300/Transformer-Implementations
https://github.com/Unbabel/COMET
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/UniversalDependencies/UD_Persian-PerDT
https://github.com/UnknownGenie/altered-BERT-KPE
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/VTOOL-R1/vtool-r1
VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use
https://github.com/VachanVY/gpt.jax
https://github.com/Vakihito/SentimentYoutube
https://github.com/Valentyn1997/semi-supervised-text-classification
https://github.com/VedantYadav/Transformer
https://github.com/VietHoang1512/khmer-nltk
https://github.com/VietHoang1710/khmer-nltk
https://github.com/VikrantChauhan001/NLP-Deep-Learning-Chatbot
https://github.com/VinAIResearch/PCC-pytorch
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
https://github.com/VincentYing/Attention-on-Attention-for-VQA
Attention on Attention: Architectures for Visual Question Answering (VQA)
https://github.com/Vladimetr/ASR-Knowledge-Transferring
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/W4ngatang/qags
https://github.com/Wadaboa/squad-question-answering
https://github.com/Walter-B/bert-20-classes
https://github.com/WantingZhao/my_GAT
https://github.com/WarruzuEndo/BiDAF_mindspore
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.