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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/simonjisu/annotated-transformer-kr
https://github.com/sjyttkl/Transformer_learning
https://github.com/socc-io/piqaboo
https://github.com/sonalisrijan/Deep-Learning-based-Defect-Prediction-Model-for-Source-Code
https://github.com/sonrisa07/transformer
https://github.com/sooftware/Attention-Implementation
https://github.com/sooftware/attentions
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/sooftware/nlp-attentions
https://github.com/soumik12345/transformer.pytorch
https://github.com/souvikshanku/translit-former
https://github.com/stevinc/Transformer_Timeseries
https://github.com/studio-ousia/luke
https://github.com/svakulenk0/response_eval
https://github.com/swimmingCreative/DeepSpeechRecognition-master
https://github.com/tanjeffreyz/attention-is-all-you-need
https://github.com/tatp22/linformer-pytorch
https://github.com/tatp22/multidim-positional-encoding
https://github.com/tbmoon/LANL_Earthquake_Prediction
https://github.com/tcnguyen/bert
https://github.com/tcxdgit/MachineTranslation_t2t
https://github.com/tensorops/TransformerX
https://github.com/text-machine-lab/transformerpy
https://github.com/thanhlong1997/bert_quora
https://github.com/the-Quert/iNLPfun
https://github.com/theQuert/inlpfun
https://github.com/thecodemasterk/BERT
https://github.com/thumt/THUMT
https://github.com/thunlp-mt/ckd
https://github.com/tjrwlgns1198/ChatBot
https://github.com/tomdyer10/fake_news
https://github.com/tree-park/kor-to-eng-translation
https://github.com/tree-park/transformer_lm
https://github.com/truclycs/ocr_seq2seq
https://github.com/tshu-w/dbcopilot
https://github.com/tshu-w/llm4em
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.