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Papers › Attention Is All You Need

Attention Is All You Need

12 Jun 2017NeurIPS 2017 12arXiv:1706.03762archive 2025-07-28

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

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microsoft/onnxruntime-training-examples mentioned on GitHubpytorch report
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mim-solutions/roberta_for_longer_texts mentioned on GitHubpytorchNOASSERTION report
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xiaopingzhong/bert-finetune-for-classfier mentioned on GitHubtfApache-2.0 report
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Attention plkmo/Transformer-Eng2French/src/models.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · 5ac38813dcddad14 · report
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Tasks

Abstractive Text SummarizationAllCoreference ResolutionDecoderFew-Shot 3D Point Cloud ClassificationImage-guided Story Ending GenerationLIDAR Semantic SegmentationLink PredictionMachine TranslationMultimodal Machine TranslationNatural Language UnderstandingQuestion AnsweringSpeech Emotion RecognitionSupervised Only 3D Point Cloud ClassificationText SummarizationTranslation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Abstractive Text Summarization CNN / Daily Mail Transformer ROUGE-1 39.50 #50 of 53 Archive leaderboard report
Abstractive Text Summarization CNN / Daily Mail Transformer ROUGE-2 16.06 #50 of 53 Archive leaderboard report
Abstractive Text Summarization CNN / Daily Mail Transformer ROUGE-L 36.63 #50 of 53 Archive leaderboard report
Constituency Parsing Penn Treebank Transformer F1 score 92.7 #23 of 27 Archive leaderboard report
Coreference Resolution Winograd Schema Challenge Subword-level Transformer LM Accuracy 54.1 #73 of 82 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Transformer BLEU-1 15.35 #3 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Transformer BLEU-2 4.49 #3 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Transformer BLEU-3 1.82 #3 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Transformer BLEU-4 0.76 #3 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Transformer CIDEr 9.32 #3 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Transformer METEOR 11.43 #3 of 4 Archive leaderboard report
Image-guided Story Ending Generation LSMDC-E Transformer ROUGE-L 19.16 #3 of 4 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Transformer BLEU-1 17.18 #4 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Transformer BLEU-2 6.29 #4 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Transformer BLEU-3 3.07 #4 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Transformer BLEU-4 2.01 #4 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Transformer CIDEr 12.75 #4 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Transformer METEOR 6.91 #4 of 6 Archive leaderboard report
Image-guided Story Ending Generation VIST-E Transformer ROUGE-L 18.23 #4 of 6 Archive leaderboard report
Machine Translation IWSLT2014 German-English Transformer BLEU score 34.44 #27 of 34 Archive leaderboard report
Machine Translation IWSLT2015 English-German Transformer BLEU score 28.50 #2 of 8 Archive leaderboard report
Machine Translation WMT2014 English-French Transformer Big BLEU score 41.0 #28 of 57 Archive leaderboard report
Machine Translation WMT2014 English-French Transformer Big Hardware Burden 23G #28 of 57 Archive leaderboard report
Machine Translation WMT2014 English-French Transformer Big Operations per network pass 2300000000.0G #28 of 57 Archive leaderboard report
Machine Translation WMT2014 English-French Transformer Base BLEU score 38.1 #40 of 57 Archive leaderboard report
Machine Translation WMT2014 English-French Transformer Base Hardware Burden 23G #40 of 57 Archive leaderboard report
Machine Translation WMT2014 English-French Transformer Base Operations per network pass 330000000.0G #40 of 57 Archive leaderboard report
Machine Translation WMT2014 English-German Transformer Big BLEU score 28.4 #44 of 91 Archive leaderboard report
Machine Translation WMT2014 English-German Transformer Big Hardware Burden 871G #44 of 91 Archive leaderboard report
Machine Translation WMT2014 English-German Transformer Big Operations per network pass 2300000000.0G #44 of 91 Archive leaderboard report
Machine Translation WMT2014 English-German Transformer Base BLEU score 27.3 #51 of 91 Archive leaderboard report
Machine Translation WMT2014 English-German Transformer Base Operations per network pass 330000000.0G #51 of 91 Archive leaderboard report
Multimodal Machine Translation Multi30K Transformer BLUE (DE-EN) 29.0 #14 of 15 Archive leaderboard report
Natural Language Understanding PDP60 Subword-level Transformer LM Accuracy 58.3 #10 of 13 Archive leaderboard report
Supervised Only 3D Point Cloud Classification ScanObjectNN Transformer GFLOPs 4.8 #11 of 12 Archive leaderboard report
Supervised Only 3D Point Cloud Classification ScanObjectNN Transformer Number of params (M) 22.1 #11 of 12 Archive leaderboard report
Supervised Only 3D Point Cloud Classification ScanObjectNN Transformer Overall Accuracy (PB_T50_RS) 77.24 #11 of 12 Archive leaderboard report
Text Summarization GigaWord Transformer ROUGE-1 37.57 #23 of 41 Archive leaderboard report
Text Summarization GigaWord Transformer ROUGE-2 18.90 #23 of 41 Archive leaderboard report
Text Summarization GigaWord Transformer ROUGE-L 34.69 #23 of 41 Archive leaderboard report

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Methods

Introduced by this paper: Absolute Position Encodings, Transformer

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerReLUResidual ConnectionSoftmaxTransformer

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