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  2. Hugging Face - Wikipedia

    en.wikipedia.org/wiki/Hugging_Face

    On September 23, 2024, to further the International Decade of Indigenous Languages, Hugging Face teamed up with Meta and UNESCO to launch a new online language translator [13] built on Meta's No Language Left Behind open-source AI model, enabling free text translation across 200 languages, including many low-resource languages.

  3. 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.

  4. React Native - Wikipedia

    en.wikipedia.org/wiki/React_Native

    React Native is an open-source UI software framework developed by Meta Platforms (formerly Facebook Inc.). [3] It is used to develop applications for Android , [ 4 ] : §Chapter 1 [ 5 ] [ 6 ] Android TV , [ 7 ] iOS , [ 4 ] : §Chapter 1 [ 6 ] macOS , [ 8 ] tvOS , [ 9 ] Web , [ 10 ] Windows [ 8 ] and UWP [ 11 ] by enabling developers to use the ...

  5. 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 ]

  6. Meta AI - Wikipedia

    en.wikipedia.org/wiki/Meta_AI

    Meta AI (formerly Facebook Artificial Intelligence Research) is a research division of Meta Platforms (formerly Facebook) that develops artificial intelligence and augmented and artificial reality technologies. Meta AI deems itself an academic research laboratory, focused on generating knowledge for the AI community, and should not be confused ...

  7. Knowledge graph embedding - Wikipedia

    en.wikipedia.org/wiki/Knowledge_graph_embedding

    The use of deep learning for knowledge graph embedding has shown good predictive performance even if they are more expensive in the training phase, data-hungry, and often required a pre-trained embedding representation of knowledge graph coming from a different embedding model.

  8. FaceNet - Wikipedia

    en.wikipedia.org/wiki/FaceNet

    The system uses a deep convolutional neural network to learn a mapping (also called an embedding) from a set of face images to a 128-dimensional Euclidean space, and assesses the similarity between faces based on the square of the Euclidean distance between the images' corresponding normalized vectors in the 128-dimensional Euclidean space.

  9. Generative adversarial network - Wikipedia

    en.wikipedia.org/wiki/Generative_adversarial_network

    use feedback to generate images and replace image search systems. [103] visualize the effect that climate change will have on specific houses. [104] reconstruct an image of a person's face after listening to their voice. [105] produces videos of a person speaking, given only a single photo of that person. [106] recurrent sequence generation. [107]