https://github.com/MindSpore-scientific-2/code-14/tree/main/lxmert
X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers
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/MindSpore-scientific-2/code-3/tree/main/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU
https://github.com/MindSpore-scientific-2/code-4/tree/main/Hyena-A-Convolutional-Neural-Network-for-Modelling-Sentences
Hyena Hierarchy: Towards Larger Convolutional Language Models
https://github.com/MindSpore-scientific-2/code-4/tree/main/QuantExplainNLP_AlgoAnalysis
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-5/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-5/tree/main/med-flamingo
https://github.com/MindSpore-scientific-2/code-8/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-8/tree/main/QuantExplainNLP_AlgoAnalysis
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-8/tree/main/med-flamingo
https://github.com/MindSpore-scientific-2/code-9/tree/main/med-flamingo
https://github.com/MindSpore-scientific/code-11/tree/main/Translation-Invariant/model
https://github.com/MindSpore-scientific/code-12/tree/main/FDDE/train-fine
https://github.com/MindSpore-scientific/code-14/tree/main/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/MindSpore-scientific/code-14/tree/main/stable-sam
Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound
https://github.com/MindSpore-scientific/code-3/tree/main/a-self-attentive-model-for-knowledge-tracing
https://github.com/MindSpore-scientific/code-5/tree/main/WinoGrande
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/MindSpore-scientific/code-5/tree/main/perceptual-score
Perceptual Score: What Data Modalities Does Your Model Perceive?
https://github.com/MindSpore-scientific/code-6/tree/main/multi-adversarial-domain-adaptation
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling
https://github.com/MindSpore-scientific/code-7/tree/main/TAPAS
https://github.com/MindSpore-scientific/code-7/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MindSpore-scientific/code-7/tree/main/gpt_j
https://github.com/MindSpore-scientific/code-8/tree/main/Translation-Invariant/model
https://github.com/Mingyuan-Zhu/BCI_practice
Deep learning-based electroencephalography analysis: a systematic review
https://github.com/MirunaPislar/multi-head-attention-labeller
https://github.com/Misoknisky/Bert-MultiGpu
https://github.com/Misterion777/DOM-LM
DOM-LM: Learning Generalizable Representations for HTML Documents
https://github.com/MobileGuru1013/ubot
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/MohamedAbdelsalam9/TT-Transformer
https://github.com/Monireh2/kg-deductive-reasoner
https://github.com/MorenoLaQuatra/bart-it
mT5: A massively multilingual pre-trained text-to-text transformer
https://github.com/MrFeelgoood/RealEstateStocksForecasting
IT5: Text-to-text Pretraining for Italian Language Understanding and Generation
https://github.com/Multi-Agent-LLMs/mallm
Multi-Agent Large Language Models for Conversational Task-Solving
https://github.com/MulukenW/test
https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/LanguageModeling
https://github.com/NVIDIA/NeMo-Curator
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.