In 2012, AlexNet demonstrated the effectiveness of large neural networks for image recognition, encouraging large artificial neural networks approach instead of older, statistical approaches. Ironically, Stinger is the working name for one of Autobot Blaster's Mini-Casette minion from the G1 Transformers: The Movie, whose robot mode is a scorpion & the live-action film version of Sideswipe. Before transformers, predecessors of attention mechanism were added to gated recurrent neural networks, such as LSTMs and gated recurrent units (GRUs), which processed datasets sequentially. Dependency on previous token computations prevented them from being able to parallelize the attention mechanism. In 1992, fast weight controller was proposed as an alternative to recurrent neural networks that can learn "internal spotlights of attention". [15] [6] 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 information about preceding tokens.

The transformer has had great success in natural language processing (NLP), for example the tasks of machine translation and time series prediction. Many large language models such as GPT-2, GPT-3, GPT-4, Claude, BERT, XLNet, RoBERTa and ChatGPT demonstrate the ability of transformers to perform a wide variety of such NLP-related tasks, and have the potential to find real-world applications. These may include: In 2020, difficulties with converging the original transformer were solved by normalizing layers before (instead of after) multiheaded attention by Xiong et al. This is called pre-LN Transformer. [29] restoring corrupted text: Thank you me to your party week. -> for inviting last where the means "end of output". The plain transformer architecture had difficulty converging. In the original paper [1] the authors recommended using learning rate warmup. That is, the learning rate should linearly scale up from 0 to maximal value for the first part of the training (usually recommended to be 2% of the total number of training steps), before decaying again.In 1990, the Elman network, using a recurrent neural network, encoded each word in a training set as a vector, called a word embedding, and the whole vocabulary as a vector database, allowing it to perform such tasks as sequence-prediction that are beyond the power of a simplemultilayer perceptron. A shortcoming of the static embeddings was that they didn't differentiate between multiple meanings of same-spelt words. [13]

In 2016, Google Translate gradually replaced the older statistical machine translation approach with the newer neural-networks-based approach that included a seq2seq model combined by LSTM and the "additive" kind of attention mechanism. They achieved a higher level of performance than the statistical approach, which took ten years to develop, in only nine months. [24] [25] The legendary Autobot commander, Optimus Prime, from The Transformers animated series includes 4 alternate hands, Ion Blaster, and Energon Axe accessories. Open the chest of Optimus Prime figure to reveal the iconic Matrix of Leadership. Transformers is a library produced by Hugging Face that supplies transformer-based architectures and pretrained models. [11] Architecture [ edit ] An illustration of main components of the transformer model from the original paper, where layers were normalized after (instead of before) multiheaded attention. Transformer layers can be one of two types, encoder and decoder. In the original paper both of them were used, while later models included only one type of them. BERT is an example of an encoder-only model; GPT are decoder-only models. Open the chest of Optimus Prime figure to reveal the Matrix of Leadership. Figure also features 4 alternate hands, Ion Blaster, and Energon Axe accessories

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Stinger's creation and the claim that it was "Inspired by Bumblebee", but improved in every way; and even to the claim that Bumblebee was ancient and ugly, and that Stinger improved it in the defects of his design; is inspired by the Stunticons, the five Decepticons Combiners created by Megatron in Transformers Generation One for the purpose of cross-cutting the name of the Autobots. And as with Bumblebee, the original Stunticons imitate five of the members who make up some of the Autobots: Motormaster (Optimus Prime's imtation), Dead End (Jazz's imitation), Breakdown (Sideswipe's imitation), Wildrider (Windcharger's imitation) and Drag Strip (Mirage's imitation). The toyline is a Walmart exclusive in the US and Canada; they were later made available on Hasbro Pulse in limited quantities. Attention ( Q , K , V ) = softmax ( Q K T d k ) V {\displaystyle {\begin{aligned}{\text{Attention}}(Q,K,V)={\text{softmax}}\left({\frac {QK Like earlier seq2seq models, the original transformer model used an encoder-decoder architecture. The encoder consists of encoding layers that process the input tokens iteratively one layer after another, while the decoder consists of decoding layers that iteratively process the encoder's output as well as the decoder output's tokens so far.

Transformers R.E.D., stylized as R.E.D. [Robot Enhanced Design], is a subline of the Generations franchise featuring six-inch scale non-transforming action figures of Transformers characters from across different franchises and continuities. Aesthetically, the line trends towards show-accuracy in its sculpts, though it's not uncommon for extra sculpted details not on the show models to pop up on some of the simpler designs. Each figure comes with multiple accessories and extra hands for more display options.SCREEN-ACCURATE DESIGN: Highly poseable with more than 75 deco ops and over 26 points of articulation, this Transformers R.E.D. figure was designed to bring collectors our most screen-accurate version of the character to display on their shelf In 2001, a one-billion-word large text corpus, scraped from the Internet, referred to as "very very large" at the time, was used for word disambiguation. [17] Stinger can be considered as the evil counterpart of Cliffjumper, as both characters bear the same body type as Bumblebee but red instead of yellow & transforms into a different brand of car. Input text is split into n-grams encoded as tokens and each token is converted into a vector via looking up from a word embedding table. At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism allowing the signal for key tokens to be amplified and less important tokens to be diminished. Though the transformer paper was published in 2017, the softmax-based attention mechanism was proposed in 2014 for machine translation, [4] [5] and the Fast Weight Controller, similar to a transformer, was proposed in 1992. [6] [7] [8]