Papers with Code 아카이브
Multimodal Machine Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Multimodal machine translation is the task of doing machine translation with multiple data sources - for example, translating "a bird is flying over water" + an image of a bird over water to German text.
<span style="color:grey; opacity: 0.6">( Image credit: Findings of the Third Shared Task on Multimodal Machine Translation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gmum/dl-mo-2021
https://github.com/going-doer/paper2code
https://github.com/goldenbili/Bert_Test2
https://github.com/goldenbili/Bert_Test3
https://github.com/goldenbili/bert_lamb_pretrain
https://github.com/goodluck4s/bert-
https://github.com/google/flax/tree/master/examples/wmt
https://github.com/gooofy/zbrain
https://github.com/gooppe/transformer-summarization
https://github.com/gordicaleksa/pytorch-original-transformer
https://github.com/graykode/gpt-2-Pytorch
https://github.com/graykode/nlp-tutorial
https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network
https://github.com/guoyaohua/BERT-Chinese-Annotation
https://github.com/guzhang480/Google_BERT
https://github.com/h4ste/oscar
https://github.com/haidershaour/bert
https://github.com/halo090770/bert
https://github.com/han-shi/SparseBERT
https://github.com/hannibal046/nanorwkv
https://github.com/harenlin/IMDB-Sentiment-Analysis-Using-BERT-Fine-Tuning
https://github.com/harshit158/paper-dots
https://github.com/harvardnlp/annotated-transformer
https://github.com/hfxunlp/transformer
https://github.com/hiun/learning-transformers
https://github.com/hoangtrungchinh/Bert-SQuAD-v2
https://github.com/hrbigelow/transformer-aiayn
https://github.com/hubiodatalab/druggen
https://github.com/huggingface/transformers
https://github.com/hyunwookl/testam
https://github.com/hyunwoongko/transformer
https://github.com/icewing1996/bert_dep
https://github.com/ictnlp/DiverseNMT
https://github.com/ictnlp/NA-MNMT
https://github.com/ictnlp/PTE-NMT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.