Browsh is a fully-modern text-based browser. It renders anything that a modern browser can; HTML5, CSS3, JS, video and even WebGL. Its main purpose is to be run on a remote server and accessed via SSH/Mosh or the in-browser HTML service in order to significantly reduce bandwidth and thus both increase browsing speeds and decrease bandwidth costs.
RETVec is a state-of-the-art text vectorizer which works directly on text inputs to create resilient classification models. Models trained with RETVec achieve better classification performance with fewer parameters and exhibit stronger resilience against adversarial attacks and typos, as reported in our paper.
Google is countering with RETVec (Resilient & Efficient Text Vectorizer). Open sourced by Google Research, this approach “helps models achieve state-of-the-art classification performance and drastically reduces computational cost,” while supporting “every language and all UTF-8 characters without the need for text preprocessing.” This makes it ideal for on-device, web, and other large-scale use cases:
With deep learning, the ROI for having clean and high quality data is immense, and this is realized in every phase of training. For context, the era right before BERT in the text classification world was one where you wanted an abundance of data, even at the expense of quality. It was more important to have representation via examples than for the examples to be perfect. This is because many Al systems did not use pre-trained embeddings (or they weren't any good, anyway) that could be leveraged by a model to apply practical generalizability. In 2018, BERT was a breakthrough for down-stream text tasks,
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