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/kirubarajan/roft
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/kirubarajan/trick
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/kit-mrt/red-motion
https://github.com/kit-mrt/road-barlow-twins
https://github.com/kitnhl/partisan-tweets-classification
https://github.com/kmeng01/rome
https://github.com/knotgrass/attention/blob/main/attn/attention.py
https://github.com/knowlab/bi-weekly-paper-presentation
https://github.com/knuddj1/op_text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/knuddy/op_text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kobowon/cs470_project_version2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kokikwbt/deepsect
https://github.com/kolloldas/torchnlp
https://github.com/kotu931226/classifier_transformer_pytorch
https://github.com/koustuvsinha/clutrr-workshop
Compositional Language Understanding with Text-based Relational Reasoning
https://github.com/kpe/bert-for-tf2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kpot/keras-transformer
https://github.com/kr900910/supreme_court_opinion
https://github.com/krafton-ai/korani
https://github.com/kramamur/sentiment-analysis
https://github.com/krantirk/BERT-Pretrained-model
https://github.com/kriesbeck/masked-language-prediction
https://github.com/krishgoel/hiro
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
https://github.com/krocki/np-transformer
https://github.com/kuldeep7688/BioMedicalBertNer
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/kunde122/bilm-tf
https://github.com/kwanhui/finrelextract
https://github.com/ky1994/SpeechRecognition
https://github.com/kyegomez/GPT3
https://github.com/kyegomez/PALI
PaLI-X: On Scaling up a Multilingual Vision and Language Model
https://github.com/kyegomez/PALI3
https://github.com/kyegomez/PALM-E
https://github.com/kyegomez/RT-2
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
https://github.com/kylemath/DeepEEG
Deep learning-based electroencephalography analysis: a systematic review
https://github.com/kyubyong/bert_ner
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.