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Text Classification 1997 [13] [14] M. Bohanec YouTube Comedy Slam Preference Dataset User vote data for pairs of videos shown on YouTube. Users voted on funnier videos. Video metadata given. 1,138,562 Text Classification 2012 [15] [16] Google Skytrax User Reviews Dataset User reviews of airlines, airports, seats, and lounges from Skytrax.
Images, Text Classification, object detection 2007 [29] [30] G. Griffin et al. COYO-700M Image–text-pair dataset 10 billion pairs of alt-text and image sources in HTML documents in CommonCrawl 746,972,269 Images, Text Classification, Image-Language 2022 [31] SIFT10M Dataset SIFT features of Caltech-256 dataset. Extensive SIFT feature extraction.
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 ]
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are generative pretrained transformers (GPTs).
Hugging Face, Inc. is a Franco-American company that develops computation tools for building applications using machine learning. It is known for its transformers library built for natural language processing applications.
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 ...
The XLNet was an autoregressive Transformer designed as an improvement over BERT, with 340M parameters and trained on 33 billion words.It was released on 19 June, 2019, under the Apache 2.0 license. [1]
OpenVINO IR [5] is the default format used to run inference. It is saved as a set of two files, *.bin and *.xml, containing weights and topology, respectively.It is obtained by converting a model from one of the supported frameworks, using the application's API or a dedicated converter.