![]() Others include Google Cloud Functions and Azure Functions, to name a few. AWS Lambda is one of many technologies that utilize a serverless architecture. The selector logic runs as an AWS Lambda function, accessible via an API endpoint. ![]() For now let’s focus on the serverless platform where the HTML selection logic is hosted. I will cover this in greater detail in another post. I’m still tweaking this selection logic, but it works on roughly 80% of the sites I’ve tested. First I find the parent node with the most children, and then extract the text based on the selector (e.g. My current approach uses a mix of HTML node counting, elimination, and DOM selection. Below I have re-written the previous Bart code to use Cortex:Ĭomparing the incorrect and correct selection in a web page. Cortex simplifies the process of deploying machine learning models to the cloud. While I could manually set this all up via the AWS Console, I deferred the manual work to an open source platform called Cortex. I ended up using an AWS virtual machine with Nvidia T4 GPU (more on this below). At this point I turned to AWS to get a souped-up cloud machine (EC2) on a cluster configured with a GPU. What’s more, the model is quite large (the “ bart-large-cnn” model is 1.5GB), so I needed to ensure it was pre-loaded before performing inference. It wasn’t immediately obvious to me, but a GPU is required to perform a faster inference. As a result, it performs best on news articles, as compared to other types of text. The model was pretrained on a huge text dataset and fine-tuned to summarize news articles. This code shows that there is already a model we can use, and we feed it the text to summarize. I will explain how each component works, starting from the summarization component on the right, and finishing with the Slack component on the left. The flow is initiated when a Slack user mentions the bot with a URL, and the data then flows through the remaining components and is ultimately returned to the Slack user as a summary. The diagram below shows what each component of the tldr bot will do. What’s great is that this model is ready to be used as a library. The post describes an improved machine learning model for achieving better performance in text generation (summarization) over previous models. ![]() The idea to write this bot came from a blog post based on the BART model created by a team at Facebook AI Research. Wouldn’t it be cool if you could get real-time summaries on-demand? You may even earn back some precious time to spend elsewhere :) Sometimes I get my fix from /r/savedyouaclick, but that list is manually curated. Oftentimes I just want to get to the bottom of a headline and decide whether it’s worth reading the entire thing. They lure you in with click-bait titles only to reward you with content that falls flat. News articles in particular are such a tease. We’re bombarded with information everyday and it’s getting harder and harder to keep up. Also a special thanks to Robert Reed, Catherine Nelson, and Hannes Hapke for helping me write this blog post. Our Word add-in allows you to generate a claim summary within Word.Props to the Concur Labs team for helping me test this bot, and for their patience while the bot sent countless debug messages their way. In my practice, I'm careful to make sure that the detailed description section (which does not include the claim summary section) describes the invention using the same words and phrases of the claims, but the claim summary section provides a good backup. The reason for creating a claim summary section is to ensure that your patent application has literal support for the claims. Not hard for a single short claim, but mind numbing when you have several pages of claims. ![]() In some aspects, the techniques described herein relate to a method for perpetual motion including magic. The claim summary section is the most tedious part of drafting a patent application.Ī method for perpetual motion comprising magic.įor the claim summary, you need to rewrite this claim like this: Patent Bots latest tool is for automatic generation of the claim summary section of your patent application. ![]()
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