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T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.
Google Translate's NMT system uses a large artificial neural network capable of deep learning. [1] [2] [3] By using millions of examples, GNMT improves the quality of translation, [2] using broader context to deduce the most relevant translation. The result is then rearranged and adapted to approach grammatically based human language. [1]
Lens can also use images to identify text and can find results from Google Search or translate the text with Google Translate in augmented reality. [9] Lens is also integrated with the Google Photos and Google Assistant apps. [5] The service originally launched as Google Goggles, a previous app that functioned similarly but with less capability.
Word Lens was an augmented reality translation application from Quest Visual. [1] Word Lens used the built-in cameras on smartphones and similar devices to quickly scan and identify foreign text (such as that found in a sign or a menu), and then translated and displayed the words in another language on the device's display.
The next sentence prediction task. Given two spans of text, the model predicts if these two spans appeared sequentially in the training corpus, outputting either [IsNext] or [NotNext]. The first span starts with a special token [CLS] (for "classify"). The two spans are separated by a special token [SEP] (for "separate").
Sentence processing takes place whenever a reader or listener processes a language utterance, either in isolation or in the context of a conversation or a text. Many studies of the human language comprehension process have focused on reading of single utterances (sentences) without context.