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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. BLOOM (language model) - Wikipedia

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

    The model, as well as the code base and the data used to train it, are distributed under free licences. [3] BLOOM was trained on approximately 366 billion (1.6TB) tokens from March to July 2022. [4] [5] BLOOM is the main outcome of the BigScience collaborative initiative, [6] a one-year-long research workshop that took place between May 2021 ...

  4. Byte pair encoding - Wikipedia

    en.wikipedia.org/wiki/Byte_pair_encoding

    Byte pair encoding [1] [2] (also known as BPE, or digram coding) [3] is an algorithm, first described in 1994 by Philip Gage, for encoding strings of text into smaller strings by creating and using a translation table. [4]

  5. Hugging Face - Wikipedia

    en.wikipedia.org/wiki/Hugging_Face

    huggingface.co Hugging Face, Inc. is an American company that develops computation tools for building applications using machine learning . It is incorporated under the Delaware General Corporation Law [ 1 ] and based in New York City .

  6. JerryScript - Wikipedia

    en.wikipedia.org/wiki/Jerryscript

    JerryScript is an ultra-lightweight JavaScript engine for the Internet of things. It is capable of executing ECMAScript 5.1 source code on devices with less than 64 KB of memory. The engine was open sourced on GitHub in June 2015. JerryScript is licensed under the Apache License 2.0.

  7. Prompt engineering - Wikipedia

    en.wikipedia.org/wiki/Prompt_engineering

    For text-to-image models, textual inversion [54] performs an optimization process to create a new word embedding based on a set of example images. This embedding vector acts as a "pseudo-word" which can be included in a prompt to express the content or style of the examples.

  8. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    In practice however, BERT's sentence embedding with the [CLS] token achieves poor performance, often worse than simply averaging non-contextual word embeddings. SBERT later achieved superior sentence embedding performance [8] by fine tuning BERT's [CLS] token embeddings through the usage of a siamese neural network architecture on the SNLI dataset.

  9. V8 (JavaScript engine) - Wikipedia

    en.wikipedia.org/wiki/V8_(JavaScript_engine)

    TurboFan compiles this bytecode into machine code. In other words, V8 compiles ECMAScript directly to native machine code using just-in-time compilation before executing it. [18] The compiled code is additionally optimized (and re-optimized) dynamically at runtime, based on heuristics of the code's execution profile.