https://github.com/simran-arora/privacy_fm
Ask Me Anything: A simple strategy for prompting language models
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/simran-khanuja/multilingual-fig-qa
Testing the Ability of Language Models to Interpret Figurative Language
https://github.com/siqims/ARC-Challenge
https://github.com/siramatu/brigade-visualizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sivandoveh/dac
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
https://github.com/sjyttkl/Transformer_learning
https://github.com/skoltech-nlp/rudetoxifier
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/skoltech-nlp/russe_detox_2022
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/skt-ai/kobart
https://github.com/skyday123/pytorch-lamb
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/skytliang/multi-agents-debate
https://github.com/skyworkai/moe-plus-plus
https://github.com/skyworkai/moh
https://github.com/sliao-mi-luku/Chatbot-Reformer
https://github.com/sliao-mi-luku/NLP-Chatbot-Reformer-Trax
https://github.com/slme1109/Lyrics_Generator_Using_LSTM
https://github.com/slme1109/lyrics-generator
https://github.com/smallbenchnlp/ELECTRA-DeBERTa
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
https://github.com/smarton-empower/smarton-ai
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/smile-data/smile
https://github.com/snap-stanford/pretrain-gnns
https://github.com/snjstudent/MyREALM
https://github.com/snoop2head/ehrxqa-2024
https://github.com/snowkylin/gnn
https://github.com/snownus/COOP
https://github.com/snucclab/EPT
https://github.com/snucclab/ept-x
https://github.com/socc-io/piqaboo
https://github.com/social-ai-studio/matk
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
https://github.com/soldni/pyterrier_sentence_transformers
Unsupervised Dense Information Retrieval with Contrastive Learning
https://github.com/somiltg/xquad
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sonalisrijan/Deep-Learning-based-Defect-Prediction-Model-for-Source-Code
https://github.com/songmzhang/dskd
https://github.com/sonrisa07/transformer
https://github.com/sooftware/Attention-Implementation
https://github.com/sooftware/attentions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.