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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/jgcbrouns/Introducing-Sparsity-in-the-Transformer
https://github.com/jiahuei/Self-Critical-SCST-TensorFlow
https://github.com/jinzhenfan/BERT
https://github.com/jmnolte/hccnet
https://github.com/jmyrberg/finnlem
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/joebluems/Soul_Of_AI
https://github.com/joongbo/tta
https://github.com/joytianya/google_bert
https://github.com/junhahyung/bert_finetune
https://github.com/junhahyung/bert_transfer
https://github.com/kaituoxu/Speech-Transformer
https://github.com/kaituoxu/SpeechTransformer
https://github.com/karenacorn99/explore-bert
https://github.com/karpathy/makemore
https://github.com/karpathy/minGPT
https://github.com/karta282950/bert-multiclass
https://github.com/keiyamashita111/aaproject
https://github.com/kelly2016/multi-label-bert
https://github.com/keonlee9420/Parallel-Tacotron2
https://github.com/kh-kim/simple-nmt
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/khanhptnk/bandit-nmt
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/khwajawisal/Attention-augmented-Convolutional-Neural-Networks
https://github.com/kiko441500/google_bert
https://github.com/kingcheng2000/bert
https://github.com/knotgrass/attention/blob/main/attn/attention.py
https://github.com/kolloldas/torchnlp
https://github.com/kotu931226/classifier_transformer_pytorch
https://github.com/kpot/keras-transformer
https://github.com/krantirk/BERT-Pretrained-model
https://github.com/kriesbeck/masked-language-prediction
https://github.com/krocki/np-transformer
https://github.com/ky1994/SpeechRecognition
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lancopku/CVST
https://github.com/ldulcic/chatbot
Effective Approaches to Attention-based Neural Machine Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.