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Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
A related technique is single-instance (data) storage, which replaces multiple copies of content at the whole-file level with a single shared copy. While possible to combine this with other forms of data compression and deduplication, it is distinct from newer approaches to data deduplication (which can operate at the segment or sub-block level).
Note that in JavaScript filter and map return a new shallow copy of the preceding array but sort operates in place. To get a similar behavior, toSorted may be used. But in this particular case, sort operates on the new array returned from filter and therefore does not change the original array.
Dataframe may refer to: A tabular data structure common to many data processing libraries: pandas (software) § DataFrames; The Dataframe API in Apache Spark;
In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.
A vector database, vector store or vector search engine is a database that can store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more Approximate Nearest Neighbor algorithms, [1] [2] [3] so that one can search the database with a query vector to retrieve the closest matching database records.
However, if data is a DataFrame, then data['a'] returns all values in the column(s) named a. To avoid this ambiguity, Pandas supports the syntax data.loc['a'] as an alternative way to filter using the index. Pandas also supports the syntax data.iloc[n], which always takes an integer n and returns the nth value, counting from 0. This allows a ...