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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/manshri/tesum

mT5: A massively multilingual pre-trained text-to-text transformer

https://github.com/maplightrx/maplight-tdc

Strategies for Pre-training Graph Neural Networks

https://github.com/marble0117/GNN_models_pytorch

Graph Attention Networks

https://github.com/marble0117/GNN_models_pytorch_geometric

Graph Attention Networks

https://github.com/marblet/GNN_models_pytorch_geometric

Graph Attention Networks

https://github.com/marblet/gat-pytorch

Graph Attention Networks

https://github.com/maroxtn/tun-sentiment

Attention Is All You Need

https://github.com/martiansideofthemoon/relic-retrieval

ELI5: Long Form Question Answering

https://github.com/martinsbruveris/tensorflow-image-models

Pay Attention to MLPs

https://github.com/masterai-eam/sciqag

SciQAG: A Framework for Auto-Generated Science Question Answering Dataset with Fine-grained Evaluation

https://github.com/matwilso/relation-networks

A simple neural network module for relational reasoning

https://github.com/maxjcohen/transformer

Attention Is All You Need

https://github.com/maxmatical/pytorch-projects

Attention Is All You Need

https://github.com/maxudong1993/mybert

Attention Is All You Need

https://github.com/mayoor/attention_network_experiments

Attention Is All You Need

https://github.com/mayurnewase/Quora-Bert

Attention Is All You Need

https://github.com/mayurnewase/Translation

Attention Is All You Need

https://github.com/mbzuai-clear/ioe-prompting

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

https://github.com/mbzuai-oryx/unimed-clip

BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

https://github.com/mbzuai-paris/lm-evaluation-harness-atlas-chat

Language Models are Few-Shot Learners

https://github.com/mcao610/Factual-Error-Correction

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

https://github.com/mchandak29/IQ-VQA

IQ-VQA: Intelligent Visual Question Answering

https://github.com/mcleonard/NLG_Autoencoder

Unsupervised Natural Language Generation with Denoising Autoencoders

https://github.com/mdda/relationships-from-entity-stream

A simple neural network module for relational reasoning

https://github.com/mechatoz/convolt

Convolutional Analysis Operator Learning: Dependence on Training Data

https://github.com/meelfy/pytorch_pretrained_bert

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/mehdibenamorr/Token-Positional-Bias

Attention Is All You Need

https://github.com/mehdibenamorr/bert-positional-bias-ner

Attention Is All You Need

https://github.com/meizi1114/bert

Attention Is All You Need

https://github.com/meliketoy/graph-cnn.pytorch

Graph Attention Networks

https://github.com/meltnur/speed

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

https://github.com/memray/OpenNMT-kpg-release

Simple Recurrent Units for Highly Parallelizable Recurrence

https://github.com/menajosep/AleatoricSent

Learned in Translation: Contextualized Word Vectors

https://github.com/menloresearch/ichigo

Chameleon: Mixed-Modal Early-Fusion Foundation Models

https://github.com/mesnico/RelationNetworks-CLEVR

A simple neural network module for relational reasoning

https://github.com/mesotron/teaching_transformers

Analysing Mathematical Reasoning Abilities of Neural Models

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