Papers with Code 아카이브
Question Answering
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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/affjljoo3581/GPT2
https://github.com/ag-sc/AMUSE
AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data
https://github.com/agentdr1/la_mil
https://github.com/agnusmaximus/Word2Bits
https://github.com/ahmedmagdiosman/compress-vqa
Dual Recurrent Attention Units for Visual Question Answering
https://github.com/ahmetumutdurmus/awd-lstm
https://github.com/ahsanabbas123/Transformer
https://github.com/ai21labs/lm-evaluation
https://github.com/aic-factcheck/aic_averitec
AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web
https://github.com/aidc-ai/ovis
Wings: Learning Multimodal LLMs without Text-only Forgetting
https://github.com/aidc-ai/parrot
Wings: Learning Multimodal LLMs without Text-only Forgetting
https://github.com/aimagelab/mammoth
SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model
https://github.com/airc-keti/ke-t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/airsplay/vimpac
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/airsplay/vokenization
https://github.com/aistairc/kirt_bert_on_abci
https://github.com/aistairc/probing_sfqa
Pretrained Transformers for Simple Question Answering over Knowledge Graphs
https://github.com/ajenningsfrankston/Dynamic-Memory-Network-Plus-master
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/ajenningsfrankston/NIPS2018_DECAPROP-master
Densely Connected Attention Propagation for Reading Comprehension
https://github.com/ajoshi80/QANet
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
https://github.com/ajs7270/placevalue-benchmark
https://github.com/ak24watch/Graph-Deeplearning-GAT
https://github.com/akanyaani/gpt-2-tensorflow2.0
https://github.com/akanyaani/minGPTF
https://github.com/akanyaani/miniLLAMA
https://github.com/akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network
https://github.com/akashe/Python-Code-Generation
https://github.com/akshathaarodi/textual_timetravel_tom
https://github.com/albpurpura/PE4IR
https://github.com/aleksac99/SQuAD-SR
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/alewarne/ExplainSecurityDNNs
Evaluating Explanation Methods for Deep Learning in Security
https://github.com/alewarne/Layerwise-Relevance-Propagation-for-LSTMs
Evaluating Explanation Methods for Deep Learning in Security
https://github.com/alexOarga/haiku-geometric
https://github.com/alexlimh/DPR_MUF
https://github.com/algharak/BERTenhance
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.