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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/google/neural-logic-machines

End-To-End Memory Networks

https://github.com/google/paxml

PaLM: Scaling Language Modeling with Pathways

https://github.com/google/seqio

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

https://github.com/gooofy/zbrain

Attention Is All You Need

https://github.com/gooppe/transformer-summarization

Attention Is All You Need

https://github.com/gordicaleksa/pytorch-GAT

Graph Attention Networks

https://github.com/gordicaleksa/pytorch-original-transformer

Attention Is All You Need

https://github.com/gouwenrui/cpe-pro-main

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

https://github.com/gpt4life/alpagasus

GPT-4 Technical Report

https://github.com/gqa-ood/gqa-ood

Roses Are Red, Violets Are Blue... but Should Vqa Expect Them To?

https://github.com/graison-thomas/TinyFinBERT

TinyBERT: Distilling BERT for Natural Language Understanding

https://github.com/grantslatton/llama.cpp

Language Models are Few-Shot Learners

https://github.com/graykode/ALBERT-Pytorch

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

https://github.com/graykode/gpt-2-Pytorch

Attention Is All You Need

https://github.com/graykode/nlp-tutorial

Attention Is All You Need

https://github.com/graykode/xlnet-Pytorch

XLNet: Generalized Autoregressive Pretraining for Language Understanding

https://github.com/greenbitai/low_bit_llama

LLaMA: Open and Efficient Foundation Language Models

https://github.com/gregdurrett/nlp-qa-finalproj

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

https://github.com/gregtozzi/deep_learning_celnav

ktrain: A Low-Code Library for Augmented Machine Learning

https://github.com/griff4692/LMC

Deep contextualized word representations

https://github.com/gryan12/chat-bot

End-To-End Memory Networks

https://github.com/gshashank84/CheXNet

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

https://github.com/guidance-ai/guidance

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

https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network

Attention Is All You Need

https://github.com/guinmoon/llmfarm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/gulucaptain/dynamictrl

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

https://github.com/guoyaohua/BERT-Chinese-Annotation

Attention Is All You Need

https://github.com/guoyaohua/BERT-Classifier

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

https://github.com/gusye1234/nano-graphrag

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

https://github.com/guzhang480/Google_BERT

Attention Is All You Need

https://github.com/h19920918/quiz_for_day06

TVQA+: Spatio-Temporal Grounding for Video Question Answering

https://github.com/h2oai/h2ogpt

h2oGPT: Democratizing Large Language Models

https://github.com/h3lio5/episodic-lifelong-learning

Episodic Memory in Lifelong Language Learning

https://github.com/h4ste/oscar

Attention Is All You Need

https://github.com/habibullah-araphat/BERT-GPU

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

https://github.com/habibullah-araphat/BERT-NER-TPU

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

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