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Your step-by-step guide — integrate similar text
Using airSlate SignNow’s eSignature any business can speed up signature workflows and eSign in real-time, delivering a better experience to customers and employees. integrate similar text in a few simple steps. Our mobile-first apps make working on the go possible, even while offline! Sign documents from anywhere in the world and close deals faster.
Follow the step-by-step guide to integrate similar text:
- Log in to your airSlate SignNow account.
- Locate your document in your folders or upload a new one.
- Open the document and make edits using the Tools menu.
- Drag & drop fillable fields, add text and sign it.
- Add multiple signers using their emails and set the signing order.
- Specify which recipients will get an executed copy.
- Use Advanced Options to limit access to the record and set an expiration date.
- Click Save and Close when completed.
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what is going on guys welcome back in today's video we're going to learn how to measure text similarity using natural language processing in python so let's get right into it [Music] all right now for this video today we're going to use an external python library called spacey and spacey is essentially a simple and easy to use natural language processing library that has a lot of different functionalities and one of those functionalities is to compare or to measure the similarity between texts or also individual words and in order to use spacey we first need to install it so we open up a command line and we type pip install spacey like this and one more thing that we need to do here is we need to also install a so-called language model because when we analyze text when we want to compare similarity when we want to do any natural language processing we need to have a basic understanding of the language the machine that we use the computer needs to have a basic understanding of the language and for that we need a so-called language model so we need to install such a language model for spacey and for that we type python and on mac and linux usually you type python3 dash m spacey download and then uh the name of the language model you can google if you want to use a different one but i'm gonna use or we're gonna use for today's video en underscore core underscore web underscore lg now lg stands for large you can also download md for medium or sm for small but we're gonna download large today so this is a 400 megabyte language model that we're going to use in spacey i'm not going to run this now because i already have this installed and as i said 400 megabytes so it takes some time to download you just run this command and it's going to download the respective language language model and then we can start with the coding now for today's video i'm going to use a jupyter notebook however don't feel obligated to use one as well you can use the python idle you can use a simple notepad you can use pycharm code in whatever you want i'm just more comfortable using individual code cells here so i'm going to start by importing spacey here and we're going to create a so-called nlp object or actually it's not an nlp object but in documentation and in tutorials you always see in spacey when you use space you use the nlp name here so nlp is going to be equal to spacey a load and then we're gonna load the model that we just installed which is en core web lg and then we can use this nlp object to do all sorts of natural language processing things and the first thing that we're going to do is we're going to compare...
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