Papers with Code 아카이브
Question Answering
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- 평가표
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- 보존된 평가 기록
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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/yanzhitech/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yaqianzhang/repeatedaugmentedrehearsal
Online Class-Incremental Continual Learning with Adversarial Shapley Value
https://github.com/yasufumy/pytorch_lstm_jump
https://github.com/ydzhang-stormstout/HAT
https://github.com/yeeeqichen/Bert
https://github.com/yellowpsyduck/OccamFusionNet
https://github.com/yenchehsiao/autonomousllmagentwithadaptingplanning
https://github.com/yeonsw/vkgdr
https://github.com/yf0726/SemeterProjectSpring19
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
https://github.com/yichigo/Chest-X-Ray
https://github.com/yifding/hetseq
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yikangshen/megablocks
ST-MoE: Designing Stable and Transferable Sparse Expert Models
https://github.com/yingsen1/unimd
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
https://github.com/yinzhangyue/eot
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/yisisang/tvshowguess
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
https://github.com/yiyangzhou/csr
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
https://github.com/yiyc-kor/bert-study
https://github.com/yiyixuxu/n-grammer-flax
https://github.com/yizhongw/tk-instruct
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/yizhongw/truthfulqa_reeval
https://github.com/ykrmm/ICLR_2020
https://github.com/ykrmm/TREMBA
https://github.com/yli-z/ml4h_are_clinical_t5_models_better_for_clinical_text
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/yly-revive/chainer-mreader
Reinforced Mnemonic Reader for Machine Reading Comprehension
https://github.com/ymcui/Chinese-ELECTRA
https://github.com/ymcui/LAMB_Optimizer_TF
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ymcui/chinese-mixtral
https://github.com/yonatanbitton/automatic_generation_of_contrast_sets_from_scene_graphs
https://github.com/yongbowin/MSMARCO-Question-Answering_annotation
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/yongbowin/OpenKP_annotation
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/yongbowin/san_mrc_annotation
Stochastic Answer Networks for Machine Reading Comprehension
https://github.com/yongqyu/ranking_paragraphs_pytorch
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering
https://github.com/yonseivnl/vlm-rlaif
ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO
https://github.com/yoongtr/Grammar-Error-Correction-with-OpenNMT-tf
https://github.com/yoreG123/Paddle-ByT5
ByT5: Towards a token-free future with pre-trained byte-to-byte models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.