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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. It is known for its transformers library built for natural language processing applications.
Hugging Face's transformers library can manipulate large language models. [4] Jupyter Notebooks can execute cells of Python code, retaining the context between the execution of cells, which usually facilitates interactive data exploration. [5] Elixir is a high-level functional programming language based on the Erlang VM. Its machine-learning ...
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.
A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot, [15] Uber's Pyro, [16] Hugging Face's Transformers, [17] PyTorch Lightning, [18] [19] and Catalyst. [20] [21] PyTorch provides two high-level features: [22] Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU)
The AOL.com video experience serves up the best video content from AOL and around the web, curating informative and entertaining snackable videos.
The response to Meta's integration of Llama into Facebook was mixed, with some users confused after Meta AI told a parental group that it had a child. [ 69 ] According to the Q4 2023 Earnings transcript, Meta adopted the strategy of open weights to improve on model safety, iteration speed, increase adoption among developers and researchers, and ...
Watsonx.ai is a platform that allows AI developers to leverage a wide range of LLMs under IBM's own Granite series and others such as Facebook's LLaMA-2, free and open-source model Mistral and many others present in Hugging Face community for a diverse set of AI development tasks.
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [ 1 ] [ 2 ] It learns to represent text as a sequence of vectors using self-supervised learning .