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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]
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.
LangChain was launched in October 2022 as an open source project by Harrison Chase, while working at machine learning startup Robust Intelligence. The project quickly garnered popularity, [3] with improvements from hundreds of contributors on GitHub, trending discussions on Twitter, lively activity on the project's Discord server, many YouTube tutorials, and meetups in San Francisco and London.
Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for building applications using machine learning.
The model, as well as the code base and the data used to train it, are distributed under free licences. [3] BLOOM was trained on approximately 366 billion (1.6TB) tokens from March to July 2022. [4] [5] BLOOM is the main outcome of the BigScience collaborative initiative, [6] a one-year-long research workshop that took place between May 2021 ...
[citation needed] EJS was inspired by templating systems like ERB ( also known as Embedded Ruby) used in Ruby on Rails, which also allows code embedding within HTML. [4] ELS was created for JavaScript developers to create server-rendered HTML pages in an easy and familiar way, likely other templating engines available in other programming ...
Nashorn is a JavaScript engine developed in the Java programming language originally by Oracle and later by the OpenJDK Community. It relies on the support for dynamically typed languages on the Java Platform (JSR 292) (a concept first realized in the experimental Da Vinci Machine and a standard part of Java 7 and later.)
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 ...