Papers with Code 아카이브
Question Answering
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- 하위 태스크
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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/philschmid/knowledge-distillation-transformers-pytorch-sagemaker
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/phixion/phixion
https://github.com/phoebussi/alpaca-cot
https://github.com/phohenecker/pytorch-transformer
https://github.com/phsfr/UD_Persian-PerDT
https://github.com/phucdev/TL_Bio_RE
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/piegu/language-models
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/pingheng001/Cnn-Bert
https://github.com/piquil/rydberggpt
https://github.com/pisalore/roberta_results
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/pku-alignment/safe-sora
BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset
https://github.com/pku-yuangroup/languagebind
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
https://github.com/pliang279/multiviz
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
https://github.com/plkmo/BERT-Relation-Extraction
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/plkmo/Transformer-Eng2French
https://github.com/pnorridge/think-global-act-local
Think Global, Act Local: Relating DNN generalisation and node-level SNR
https://github.com/pokaxpoka/RAD_state_aug
https://github.com/pokaxpoka/rad_openaigym
https://github.com/polzinben/TensorFlow_NLP
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/pomonam/AttentionCluster
https://github.com/postech-ami/smile-dataset
https://github.com/potsawee/selfcheckgpt
https://github.com/ppasupat/WikiTableQuestions
https://github.com/pprabh2007/temp
Pre-trained Language Model for Biomedical Question Answering
https://github.com/pps-lab/fl-analysis
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
https://github.com/prabhupad26/100daysofML
https://github.com/prajjwal1/adaptive_transformer
Reducing Transformer Depth on Demand with Structured Dropout
https://github.com/prajjwal1/fluence
Reducing Transformer Depth on Demand with Structured Dropout
https://github.com/pramodkaushik/np_analysis
Learning a Natural Language Interface with Neural Programmer
https://github.com/prashil2792/Question-Answering-System-Deep-Learning
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/princeton-nlp/SimCSE
https://github.com/princeton-nlp/helmet
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/princeton-nlp/transformerprograms
https://github.com/princeton-vl/Princeton365
https://github.com/pritam1322/Machine-Translation-with-Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.