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Visual Storytelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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<span style="color:grey; opacity: 0.6">( Image credit: No Metrics Are Perfect )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/leejieun51/transformer
https://github.com/lehinevych/SAAN
https://github.com/lehoanganh298/BERT-Question-Answering
https://github.com/lenacabrera/gb_mnmt
https://github.com/lennonzurich/lalala
https://github.com/lidongliang666/cv_deep_learning
https://github.com/lif31up/attention-is-all-you-need
https://github.com/lif31up/extended-BERT-for-low-rank-adaption
https://github.com/liqichen6688/duo-attention
https://github.com/littell/NamedTransformer
https://github.com/llx666/git_demo
https://github.com/longbowking/bert
https://github.com/lovedavidsilva/bert_old_version
https://github.com/luckynozomi/PPI_Bert
https://github.com/lucylow/En_francais_si_vous_plait-
https://github.com/lukemelas/PyTorch-Pretrained-ViT
https://github.com/lvyufeng/mindspore-nlp-tutorial
https://github.com/magahub/songrnn
https://github.com/majing2019/transformer
https://github.com/manideep2510/siamese-BERT-fake-news-detection-LIAR
https://github.com/maroxtn/tun-sentiment
https://github.com/maxjcohen/transformer
https://github.com/maxmatical/pytorch-projects
https://github.com/maxudong1993/mybert
https://github.com/mayoor/attention_network_experiments
https://github.com/mayurnewase/Quora-Bert
https://github.com/mayurnewase/Translation
https://github.com/mehdibenamorr/Token-Positional-Bias
https://github.com/mehdibenamorr/bert-positional-bias-ner
https://github.com/meizi1114/bert
https://github.com/mhamedLmarbouh/Attention-is-all-you-need
https://github.com/mhaseeb123/DeepMSim
https://github.com/microcoder-py/attn-is-all-you-need
https://github.com/microsoft/onnxruntime-training-examples
https://github.com/mike-a-yen/date-translation
Effective Approaches to Attention-based Neural Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.