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[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 ...
huggingface.co 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 .
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.
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]
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.
Pair programming allows team members to share quickly, making them less likely to have agendas hidden from each other. This helps pair programmers learn to communicate more easily. "This raises the communication bandwidth and frequency within the project, increasing overall information flow within the team." [3]
The project aim will be to allow embedding JavaScript in Java applications via JSR-223 and to develop standalone JavaScript applications. [7] On December 21, 2012, Oracle announced Nashorn source was publicly released in the OpenJDK repository. [8] It provides a 100% support of ECMAScript 5.1. [9]
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.