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A word n-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been superseded by large language models. [1] It is based on an assumption that the probability of the next word in a sequence depends only on a fixed size window of previous words.
The following chart lists countries and dependencies along with their capital cities, in English and non-English official language(s). In bold: internationally recognized sovereign states. The 193 member states of the United Nations (UN) Vatican City (administered by the Holy See, a UN observer state), which is generally recognized as a ...
A language that uniquely represents the national identity of a state, nation, and/or country and is so designated by a country's government; some are technically minority languages. (On this page a national language is followed by parentheses that identify it as a national language status.) Some countries have more than one language with this ...
37.5 million image-text examples with 11.5 million unique images across 108 Wikipedia languages. 11,500,000 image, caption Pretraining, image captioning 2021 [11] Srinivasan e al, Google Research Visual Genome Images and their description 108,000 images, text Image captioning 2016 [12] R. Krishna et al. Berkeley 3-D Object Dataset
Figure 1 shows several example sequences and the corresponding 1-gram, 2-gram and 3-gram sequences. Here are further examples; these are word-level 3-grams and 4-grams (and counts of the number of times they appeared) from the Google n-gram corpus. [4] 3-grams ceramics collectables collectibles (55) ceramics collectables fine (130)
This is a list of official, or otherwise administratively-recognized, languages of sovereign countries, regions, and supra-national institutions. The article also lists lots of languages which have no administrative mandate as an official language, generally describing these as de facto official languages.
For languages written in other writing systems, write "Romanization - native script (language)", for example "Argentine - אַרגענטינע (Yiddish)", and alphabetize it in the list by the Romanized form. Due to its size, this list has been split into four parts: List of country names in various languages (A–C)
The query likelihood model is a language model [1] [2] used in information retrieval. A language model is constructed for each document in the collection. It is then possible to rank each document by the probability of specific documents given a query. This is interpreted as being the likelihood of a document being relevant given a query.