Papers with Code 아카이브

Question Answering

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하위 태스크: 19
평가표: 153
보존된 평가 기록: 1,781

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/jainshobhit/Variational-Autoencoder

Neural Variational Inference for Text Processing

https://github.com/jaisenbe58r/NLP-Transformer_Translator

Attention Is All You Need

https://github.com/jaketae/g-mlp

Pay Attention to MLPs

https://github.com/jaketae/pytorch-malware-detection

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

https://github.com/jamesdvance/predicting_clicks

Attention Is All You Need

https://github.com/jangjoongkeon/JK

Attention Is All You Need

https://github.com/jankrepl/mildlyoverfitted

PonderNet: Learning to Ponder

https://github.com/jastfkjg/semantic-matching

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

https://github.com/jbdel/OMG_UMONS_submission

Attention Is All You Need

https://github.com/jbeno/sentiment

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

https://github.com/jdai-cv/image-captioning

X-Linear Attention Networks for Image Captioning

https://github.com/jdxyw/deepKT

A Self-Attentive model for Knowledge Tracing

https://github.com/jean-kunz/ml_research_papers

Attention Is All You Need

https://github.com/jelifysh/Transformers

Attention Is All You Need

https://github.com/jeongchanyoung-1234/De-En-NMT

Attention Is All You Need

https://github.com/jfhealthcare/Chexpert

ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

https://github.com/jgcbrouns/Introducing-Sparsity-in-the-Transformer

Attention Is All You Need

https://github.com/jiachangliu/kategpt3

What Makes Good In-Context Examples for GPT-$3$?

https://github.com/jiacheng-xu/text-sum-uncertainty

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/jiacheng-xu/vmf_vae_nlp

Neural Variational Inference for Text Processing

https://github.com/jiahuei/Self-Critical-SCST-TensorFlow

Attention Is All You Need

https://github.com/jialinwu17/Self_Critical_VQA

Self-Critical Reasoning for Robust Visual Question Answering

https://github.com/jialinwu17/tmpimgs

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

https://github.com/jianjieluo/openai-clip-feature

How Much Can CLIP Benefit Vision-and-Language Tasks?

https://github.com/jiaowoguanren0615/MambaVision

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

https://github.com/jiaruncao/BioCopyMechanism

Unified Language Model Pre-training for Natural Language Understanding and Generation

https://github.com/jiasenlu/vilbert_beta

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

https://github.com/jiho-yesnlp/qytl

SQuAD: 100,000+ Questions for Machine Comprehension of Text

https://github.com/jimfleming/recurrent-entity-networks

Tracking the World State with Recurrent Entity Networks

https://github.com/jimmy6727/Informd

Distributed Representations of Sentences and Documents

https://github.com/jingtaozhan/DRhard

Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

https://github.com/jingtaozhan/JPQ

Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

https://github.com/jingtaozhan/LearningToRetrieve

Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

https://github.com/jingyaogong/minimind

Mixtral of Experts

https://github.com/jinmang2/RetroReader

Retrospective Reader for Machine Reading Comprehension

https://github.com/jinzhenfan/BERT

Attention Is All You Need

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.