Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/mocherson/aki_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/modar7/the_fairness_stitch
Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks
https://github.com/modelscope/modelscope
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
https://github.com/moduIo/Relation-Networks
https://github.com/moein-shariatnia/Pix2Seq
https://github.com/moh833/VQA
https://github.com/mohamedateya/bert-fine-tuning-sentence-classification-for-cola
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/monarch-initiative/curate-gpt
MapperGPT: Large Language Models for Linking and Mapping Entities
https://github.com/monimoyd/NLPEnglishToPythonCodeUsingTransformer
https://github.com/monimoyd/NLPMachineTranslationUsingTransformer
https://github.com/monologg/distilkobert
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/monologg/kobigbird
https://github.com/moon23k/Scheduled_Sampling
https://github.com/moon23k/Transformer_Anchors
https://github.com/moon23k/Transformer_Archs
https://github.com/moutaigua8183/BookQA
Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
https://github.com/mpandeydev/SDnetmod
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
https://github.com/mramly/CairoMent
AraBERT: Transformer-based Model for Arabic Language Understanding
https://github.com/mrlab-ai/NL2Plan
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
https://github.com/mrzjy/sunburst
https://github.com/mseitzer/csmri-refinement
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
https://github.com/msfasha/Arabic-NLP
AraBERT: Transformer-based Model for Arabic Language Understanding
https://github.com/msorkhpar/wiki-entity-summarization-preprocessor
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/mthcom/hscore-dataset-pruning
https://github.com/mts-ai/replaceme
https://github.com/mtzcorporations/nlp_teamjodka
https://github.com/musixmatchresearch/umberto
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/mustafa-yavuz/chatbot-qa
https://github.com/mwbrulhardt/yelp-absa
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence
https://github.com/myamamoto555/tf-bert
https://github.com/nachiketaa/BERT-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/nachiketaa/bert
https://github.com/nageshsinghc4/deepwrap
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/nalbarr/coursera-ai4med-course3
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/nameoverflow/neuro-cangjie
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.