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  2. BLOOM (language model) - Wikipedia

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

    BigScience Large Open-science Open-access Multilingual Language Model (BLOOM) [1] [2] is a 176-billion-parameter transformer-based autoregressive large language model (LLM). The model, as well as the code base and the data used to train it, are distributed under free licences. [ 3 ]

  3. T5 (language model) - Wikipedia

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

    [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. T5 models are usually pretrained on a massive dataset of text and code, after which they can perform the text-based tasks that are similar to their pretrained tasks.

  4. Hugging Face - Wikipedia

    en.wikipedia.org/wiki/Hugging_Face

    The company was named after the U+1F917 珞 HUGGING FACE emoji. [2] After open sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning. In March 2021, Hugging Face raised US$40 million in a Series B funding round. [3]

  5. Word embedding - Wikipedia

    en.wikipedia.org/wiki/Word_embedding

    In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis . Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [ 1 ]

  6. Sentence embedding - Wikipedia

    en.wikipedia.org/wiki/Sentence_embedding

    Download QR code; Print/export ... a sentence embedding is a representation of a sentence as a vector of numbers which encodes meaningful semantic information. [1] [2 ...

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  8. Latent space - Wikipedia

    en.wikipedia.org/wiki/Latent_space

    A latent space, also known as a latent feature space or embedding space, is an embedding of a set of items within a manifold in which items resembling each other are positioned closer to one another. Position within the latent space can be viewed as being defined by a set of latent variables that emerge from the resemblances from the objects.

  9. Embedding - Wikipedia

    en.wikipedia.org/wiki/Embedding

    An embedding, or a smooth embedding, is defined to be an immersion that is an embedding in the topological sense mentioned above (i.e. homeomorphism onto its image). [ 4 ] In other words, the domain of an embedding is diffeomorphic to its image, and in particular the image of an embedding must be a submanifold .