Papers with Code 아카이브

Question Answering

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/hadi-hosseini/t2i-fineeval

T2I-FineEval: Fine-Grained Compositional Metric for Text-to-Image Evaluation

https://github.com/haidershaour/bert

Attention Is All You Need

https://github.com/haisongzhang/roberta-tiny-cased

RoBERTa: A Robustly Optimized BERT Pretraining Approach

https://github.com/haiyang-w/git

Language Models are Few-Shot Learners

https://github.com/halo090770/bert

Attention Is All You Need

https://github.com/hamedhematian/importantnews

PCoQA: Persian Conversational Question Answering Dataset

https://github.com/hamishivi/easylm

LLaMA: Open and Efficient Foundation Language Models

https://github.com/hammoudhasan/diffclip

Differential Transformer

https://github.com/han-shi/SparseBERT

Attention Is All You Need

https://github.com/handasontam/GAT-with-edgewise-attention

Graph Attention Networks

https://github.com/hanhpt23/silvar

SilVar: Speech Driven Multimodal Model for Reasoning Visual Question Answering and Object Localization

https://github.com/hannaabiakl/psychic

PSYCHIC: A Neuro-Symbolic Framework for Knowledge Graph Question-Answering Grounding

https://github.com/hannibal046/nanorwkv

Attention Is All You Need

https://github.com/hannibal046/pluglm

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

https://github.com/haonan-li/cmmlu

BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer

https://github.com/happen2me/kqa-pro-datasets

KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base

https://github.com/harenlin/IMDB-Sentiment-Analysis-Using-BERT-Fine-Tuning

Attention Is All You Need

https://github.com/harshit158/paper-dots

Attention Is All You Need

https://github.com/harvardnlp/annotated-transformer

Attention Is All You Need

https://github.com/hate-alert/indicabusive

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

https://github.com/haydlite/sparse-bert-ner

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

https://github.com/hazyresearch/legalbench

Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation

https://github.com/hcplab-sysu/causal-vlreasoning

Visual Causal Scene Refinement for Video Question Answering

https://github.com/hegebharat/SelfDiscovery

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

https://github.com/hegebharat/sentiment-Analysis-for-German-Datasets

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

https://github.com/helboukkouri/character-bert

Deep contextualized word representations

https://github.com/heraclex12/vietpunc

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

https://github.com/hexuandeng/drpruning

Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning

https://github.com/heylinsir/semantic-based-qa

A Semantic-based Method for Unsupervised Commonsense Question Answering

https://github.com/hfxunlp/transformer

Attention Is All You Need

https://github.com/hi-archers/stableke

MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions

https://github.com/hieudepchai/BERT_IE

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

https://github.com/hieunghia-pat/UIT-MCAN

Deep Modular Co-Attention Networks for Visual Question Answering

https://github.com/hihellohowareyou/RESREF_Chatbot_data_for_Korean

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

https://github.com/hilberthit/gpt-3

Language Models are Few-Shot Learners

https://github.com/hit-scir/chinese-mixtral-8x7b

Mixtral of Experts

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