Transformers State Of The Art Natural Language Processing

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen... [Transformers: State-of-the-Art Natural Language Processing](https://aclanthology.org/2020.emnlp-demos.6/) (Wolf et al., EMNLP 2020) ACL materials are Copyright © 1963–2025 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.

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🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+... This is the documentation of our repository transformers. You can also follow our online course that teaches how to use this library, as well as the other libraries developed by Hugging Face and the Hub. Low barrier to entry for educators and practitioners In deep learning, the transformer is an artificial neural network architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called tokens, and each token is converted into a... Transformers have the advantage of having no recurrent units, therefore requiring less training time than earlier recurrent neural architectures (RNNs) such as long short-term memory (LSTM).[2] Later variations have been widely adopted for training...

The modern version of the transformer was proposed in the 2017 paper "Attention Is All You Need" by researchers at Google.[1] The predecessors of transformers were developed as an improvement over previous architectures for... They are used in large-scale natural language processing, computer vision (vision transformers), reinforcement learning,[6][7] audio,[8] multimodal learning, robotics,[9] and even playing chess.[10] It has also led to the development of pre-trained systems, such as... For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable... A key breakthrough was LSTM (1995),[note 1] an RNN which used various innovations to overcome the vanishing gradient problem, allowing efficient learning of long-sequence modelling.

One key innovation was the use of an attention mechanism which used neurons that multiply the outputs of other neurons, so-called multiplicative units.[13] Neural networks using multiplicative units were later called sigma-pi networks[14] or... However, LSTM still used sequential processing, like most other RNNs.[note 2] Specifically, RNNs operate one token at a time from first to last; they cannot operate in parallel over all tokens in a sequence.

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