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The Hugging Face Hub is a platform (centralized web service) for hosting: [18] Git-based code repositories, including discussions and pull requests for projects. models, also with Git-based version control; datasets, mainly in text, images, and audio;
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.
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 ]
The use of different model parameters and different corpus sizes can greatly affect the quality of a word2vec model. Accuracy can be improved in a number of ways, including the choice of model architecture (CBOW or Skip-Gram), increasing the training data set, increasing the number of vector dimensions, and increasing the window size of words ...
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.
Ajax4jsf became an open-source project hosted on java.net, while RichFaces became a commercial JSF component library. In March 2007 JBoss (a division of Red Hat from 2006) and Exadel signed a partnership agreement whereby Ajax4jsf and RichFaces would come under the JBoss umbrella as "JBoss Ajax4jsf" and as "JBoss RichFaces".
Java Embedding Plugin (JEP) enables Java on Mac OS X with non-Safari browsers. This plugin is shipped with Firefox 1.5 on, and all recent versions of SeaMonkey and Camino. [1] The latest released version, 0.9.7.5, requires Mac OS X 10.4.11 or higher. [1]
These models, during the embedding procedure, only use the single facts to compute the embedded representation and ignore the other associations to the same entity or relation. [ 18 ] DistMult [ 19 ] : Since the embedding matrix of the relation is a diagonal matrix, [ 5 ] the scoring function can not distinguish asymmetric facts.