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2. T5 (language model) - Wikipedia

   en.wikipedia.org/wiki/T5_(language_model)

   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.

3. Autocorrection - Wikipedia

   en.wikipedia.org/wiki/Autocorrection

   Autocorrect in Windows 10, correcting the word "mispelled" to "misspelled".. Autocorrection, also known as text replacement, replace-as-you-type, text expander or simply autocorrect, is an automatic data validation function commonly found in word processors and text editing interfaces for smartphones and tablet computers.

4. Google Neural Machine Translation - Wikipedia

   en.wikipedia.org/wiki/Google_Neural_Machine...

   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]

5. Word Lens - Wikipedia

   en.wikipedia.org/wiki/Word_Lens

   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.

6. Google Lens - Wikipedia

   en.wikipedia.org/wiki/Google_Lens

   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.

7. BERT (language model) - Wikipedia

   en.wikipedia.org/wiki/BERT_(language_model)

   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").