https://github.com/bangoc123/retrieval-backend-with-rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Papers with Code 아카이브
Question Answering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/bangoc123/transformer
https://github.com/baosongyang/WRD
https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT
https://github.com/bartzi/chainer-transformer
https://github.com/basile-bron/Artificial-Intelligence-Lexical
https://github.com/basiralab/RG-Select
https://github.com/basiralab/Reproducible-Generative-Learning
https://github.com/batsresearch/alfred
https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/bcaitech1/p2-klue-Heeseok-Jeong
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bcmi220/esc4nmt
https://github.com/bdusell/stack-attention
https://github.com/beir-cellar/beir
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
https://github.com/benjamintaiwo/Attention
https://github.com/bennati/baldwin_veering
https://github.com/benprano/Question-Answering-Deep-Learning
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
https://github.com/bentrevett/pytorch-sentiment-analysis
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/benywon/Chinese-GPT-2
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
https://github.com/benywon/ChineseBert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/beomi/koalpaca
https://github.com/berniwal/DeepLearningProject
https://github.com/besser-pearl/agentic-bpmn
https://github.com/bestend/tf2-bi-lstm-crf-nni
https://github.com/bfopengradient/NLP_ROBERTA
https://github.com/bfopengradient/NLP_text_summarization_apps
https://github.com/bhavitvyamalik/bert
https://github.com/bigcode-project/starcoder
https://github.com/bigscience-workshop/Megatron-DeepSpeed
https://github.com/bigscience-workshop/architecture-objective
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/bigscience-workshop/promptsource
https://github.com/billxbf/rewoo
https://github.com/binhetech/bert-application
https://github.com/bino282/bert4news
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/binwang28/rse
What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.