https://github.com/MS-Mind/MS-Code-01/tree/main/seq2seq
Lossless Acceleration for Seq2seq Generation with Aggressive Decoding
Papers with Code 아카이브
Machine Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Machine translation is the task of translating a sentence in a source language to a different target language.
Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.
One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.
<span style="color:grey; opacity: 0.6">( Image credit: Google seq2seq )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MS-P3/code5/tree/main/t5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/MS-P3/code6/tree/main/finetune
https://github.com/MS-P3/code7/tree/main/xlm
https://github.com/MS-P3/code7/tree/main/xlm_roberta
https://github.com/MaZhiyuanBUAA/bert-tf1.4.0
https://github.com/Maab-Nimir/Neural-Machine-Translation-by-Jointly-Learning-to-Align-and-Translate
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/ManzoorElahi/organic-chemistry-reaction-prediction-using-NMT
Asynchronous Bidirectional Decoding for Neural Machine Translation
https://github.com/Maple728/transformer
https://github.com/MaratSaidov/source-code-summarization
https://github.com/MarcBS/keras
https://github.com/MashaKubyshina/learning_ML
https://github.com/MattKleinsmith/pbt
https://github.com/Matthewdowney18/Transformer_Dialogue
https://github.com/Matthewdowney18/Yelp_attention
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/Matthewdowney18/Yelp_seq2seq
Neural Machine Translation by Jointly Learning to Align and Translate
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/On_the_Variance_of_the_Adaptive_Learning_Rate_and_Beyond
https://github.com/Mayurji/Image-Classification-PyTorch
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/Maz101/Bert
https://github.com/Maz101/pretrainingdataset
https://github.com/MehmetBarutcu/PHM-Paper-Implementation
https://github.com/MehranTaghian/DQN-Trading
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules
https://github.com/MichaelZhouwang/LMlexsub
https://github.com/Microsoft/NPMT
https://github.com/Mind23-2/MindCode-132
https://github.com/Mind23-2/MindCode-138
https://github.com/Mind23-2/MindCode-159
https://github.com/Mind23-2/MindCode-160
https://github.com/Mind23-2/MindCode-73
https://github.com/MindCode-4/code-11/tree/main/Fixup-Initialization
Fixup Initialization: Residual Learning Without Normalization
https://github.com/MindCode-4/code-13/tree/main/res_mlp_ms
ResMLP: Feedforward networks for image classification with data-efficient training
https://github.com/MindCode-4/code-13/tree/main/wide-minima-density-hypothesis
Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule
https://github.com/MindCode-4/code-2/tree/main/persimmon
Transformers without Tears: Improving the Normalization of Self-Attention
https://github.com/MindCode-4/code-3/tree/main/fsmt
Multi-Task Neural Models for Translating Between Styles Within and Across Languages
https://github.com/MindCode-4/code-5/tree/main/transformer
https://github.com/MindCode-4/code-7/tree/main/Fixup-Initialization
Fixup Initialization: Residual Learning Without Normalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.