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Byte pair encoding [1] [2] (also known as BPE, or digram coding) [3] is an algorithm, first described in 1994 by Philip Gage, for encoding strings of text into smaller strings by creating and using a translation table. [4]
The Hugging Face Hub is a platform (centralized web service) for hosting: [19]. Git-based code repositories, including discussions and pull requests for projects.; models, also with Git-based version control;
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]
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.
Pair programming Pair Programming, 2009. Pair programming is a software development technique in which two programmers work together at one workstation. One, the driver, writes code while the other, the observer or navigator, [1] reviews each line of code as it is typed in. The two programmers switch roles frequently.
Based on the type of tags assigned to questions, the top eight most discussed topics on the site are: JavaScript, Java, C#, PHP, Android, Python, jQuery, and HTML. [ 17 ] History
300-long word embedding vector. The vectors are usually pre-calculated from other projects such as GloVe or Word2Vec. h 500-long encoder hidden vector. At each point in time, this vector summarizes all the preceding words before it. The final h can be viewed as a "sentence" vector, or a thought vector as Hinton calls it. s
Embedding vectors created using the Word2vec algorithm have some advantages compared to earlier algorithms [1] such as those using n-grams and latent semantic analysis. GloVe was developed by a team at Stanford specifically as a competitor, and the original paper noted multiple improvements of GloVe over word2vec. [ 9 ]