Methods › Natural Language Processing › Autoregressive Transformers › Transformer

Transformer

13,999 papers tagged archive 2025-07-28

Introduced by Ashish Vaswani et al. in Attention Is All You Need

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

A Transformer is a model architecture that eschews recurrence and instead relies entirely on an attention mechanism to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of attention mechanisms allows for significantly more parallelization than methods like RNNs and CNNs.

PaperSourceSee Code · tunz/transformer-pytorch

Papers archive 2025-07-28

30 shown of 13,999, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 2,170 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Language Modelling1,237
Decoder1,068
Language Modeling949
Translation758
Machine Translation718
Semantic Segmentation679
Object Detection552
Image Classification524
Question Answering514
object-detection497
Retrieval455
Sentence454
Segmentation448
Representation Learning428
image-classification414
Large Language Model409
Time Series377
Object352
Text Generation317
Transfer Learning294

Usage over time archive 2025-07-28

Papers per year tagged with Transformer: 2017 to 2025, peak 4,361 4,361 0 2017: 21 papers 2017 2018: 115 papers 2018 2019: 510 papers 2019 2020: 824 papers 2020 2021: 1476 papers 2021 2022: 1883 papers 2022 2023: 3228 papers 2023 2024: 4361 papers 2024 2025: 1581 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (13,999 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Autoregressive TransformersTransformers

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