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  2. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    BERT pioneered an approach involving the use of a dedicated [CLS] token prepended to the beginning of each sentence inputted into the model; the final hidden state vector of this token encodes information about the sentence and can be fine-tuned for use in sentence classification tasks. In practice however, BERT's sentence embedding with the ...

  3. CLPython - Wikipedia

    en.wikipedia.org/wiki/CLPython

    CLPython is an implementation of the Python programming language written in Common Lisp. This project allow to call Lisp functions from Python and Python functions from Lisp. Licensed under LGPL. CLPython was started in 2006, but as of 2013, it was not actively developed and the mailing list was closed. [1]

  4. BERT (language model) - Wikipedia

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

    Unlike previous models, BERT is a deeply bidirectional, unsupervised language representation, pre-trained using only a plain text corpus. Context-free models such as word2vec or GloVe generate a single word embedding representation for each word in the vocabulary, whereas BERT takes into account the context for each occurrence of a given word ...

  5. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  6. ML (programming language) - Wikipedia

    en.wikipedia.org/wiki/ML_(programming_language)

    ML (Meta Language) is a general-purpose, high-level, functional programming language.It is known for its use of the polymorphic Hindley–Milner type system, which automatically assigns the data types of most expressions without requiring explicit type annotations (type inference), and ensures type safety; there is a formal proof that a well-typed ML program does not cause runtime type errors. [1]

  7. Common Lisp Object System - Wikipedia

    en.wikipedia.org/wiki/Common_Lisp_Object_System

    CL programmers use the language's package facility to declare which functions or data structures are intended for export. Apart from normal ("primary") methods, there also are :before, :after, and :around "auxiliary" methods. The former two are invoked prior to, or after the primary method, in a particular order based on the class hierarchy.

  8. Large language model - Wikipedia

    en.wikipedia.org/wiki/Large_language_model

    A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. As language models , LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process.

  9. Python syntax and semantics - Wikipedia

    en.wikipedia.org/wiki/Python_syntax_and_semantics

    A snippet of Python code with keywords highlighted in bold yellow font. The syntax of the Python programming language is the set of rules that defines how a Python program will be written and interpreted (by both the runtime system and by human readers). The Python language has many similarities to Perl, C, and Java. However, there are some ...