https://github.com/JunnYu/x-transformers-paddle
Transformers without Tears: Improving the Normalization of Self-Attention
Papers with Code 아카이브
Machine Translation
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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/JunnYu/xlm_paddle
https://github.com/KAGUYAHONGLAI/SRC
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/KaiyuanZh/censor
https://github.com/KakaoBrain/torchgpipe
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
https://github.com/Kakoedlinnoeslovo/fairseq
https://github.com/Katsumata420/bert_mlm
https://github.com/KeithGalli/pycon2020
https://github.com/Kelechukwu1/PidginUNMT
https://github.com/Kevin-Vora/bert-embedding-gluonnlp-edit-
https://github.com/KevinYoung98/Seq2Seq-PyTorch
Effective Approaches to Attention-based Neural Machine Translation
https://github.com/Khalid-Jamal/annotated-transformer-Arabic
https://github.com/Ki6an/fastT5
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/Kirankumaraswamy/Transformers
https://github.com/Kitsunetic/Optimizer-Visualization
https://github.com/KrishnaTarun/Deep-Learning-Lab
https://github.com/KuldeepSangwan/MachineTranslation
Neural Machine Translation for Low-Resourced Indian Languages
https://github.com/KushGrandhi/Polaroid
https://github.com/Kyubyong/transformer
https://github.com/LawrenceDuan/myLASER
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
https://github.com/Lemoners/Attention-Is-All-You-Need
https://github.com/LeoLaugier/conditional-auto-encoder-text-to-text-transfer-transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/LeoWood/bert-horovod
https://github.com/LiamMaclean216/Pytorch-Chatbot
https://github.com/LiamMaclean216/Pytorch-Transfomer
https://github.com/Liut2016/stackedResBiLSTM
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
https://github.com/LiyuanLucasLiu/Transformer-Clinic
https://github.com/LoveYang/bert_test
https://github.com/Lsdefine/attention-is-all-you-need-keras
https://github.com/Luccadoremi/Model-Compression-DAQ
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
https://github.com/Lukasz-G/Hydra
https://github.com/MCLYang/RhythmGAN_pytorch
https://github.com/MChen9/TCN
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://github.com/MJ-Jang/Paraphrase-Thought
Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition
https://github.com/MJ-Jang/RNN_SVAE
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.