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This Python script helps you better understand how embeddings work for SEO. Input a sentence + a query and BERT will calculate a content "Similarity Score": Search engines use embedding models to translate your content into numeric values. This is how they're able to mathematically determine whether a page... on your site is actually relevant to a query someone is searching for. Once both the content and query are run through embeddings - they can calculate the "cosine similarity" or how similar the two entities are to each one. With this Python script, you can actually visualize how cosine similarity works for yourself. To you use you'll simply: 1. Save the Python script to a text file 2. Use your Terminal to run the script 3. You'll be prompted for both a "Sentence" and "Query" 4. Once entered, the script will calculate a "Similarity Score" between the text and query. This is how relevant your sentence is for the target keyword. By running this, you'll see that search engines are able to come with an calculation of content relevance. You'll need to think about the content on your site the same way - which sections have high scores and which ones have low ones? I've linked the Python script in the comments below. No coding knowledge is required and it walks you step by step on how to implement it.show more

Chris Long

13,218 subscribers

13,185 次观看 • 1 年前 •via X (Twitter)

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5 条评论

Chris Long 的头像

Chris Long1 年前

Link to the blog article with the script below:

Daniela Fernandes 的头像

Daniela Fernandes1 年前

Olha que legal esse perfil @rodrigowperes

Traxex 的头像

Traxex1 年前

this a great stuff. Helps a lot for on-page work when we only need to revise specific part of the content and not everything.

Freelancers HUB 的头像

Freelancers HUB1 年前

Is there any way to check longer content?

Suttle 的头像

Suttle1 年前

Is the chrome extension mentioned in the blog publicly available?

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Santiago

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Zach Wilson

20,368 次观看 • 11 个月前

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Andrew Ng

104,371 次观看 • 2 年前

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echo.hive

126,688 次观看 • 1 年前

We just released "Large Language Models with Semantic Search”, built with Cohere, and taught by Jay Alammar and Luis Serrano. Search is a key part of many applications. Say, you need to retrieve documents or products in response to a user query; how can LLMs help? You’ll learn about (i) Embeddings, to retrieve a collection of documents loosely related to a query, and (ii) LLM assisted re-ranking, to rank them precisely according to relevance. You’ll also go through code showing how to tie all this together to build a complete search system for retrieving relevant Wikipedia articles. Please check it out!

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Andrew Ng

590,229 次观看 • 2 年前

(Tilt your phone to see better) If you have watched MR robot you'll remember there was a part where the company Elliot was pentesing for was under attack and the attackers planted a back door in the system and told him not to delete it. Well in this video I'm showing you how they did it. They got in through the ssh port. So in the video I'm just scanning with nmap and brute forcing with a python script, then after the successful brute force attack I log in and plant the back door which is a bash script, then I open another terminal to start a listener so we can catch the connection from the victim machine and even when I restarted the listener we still caught the connection. This is how fsociety did it. I remain root

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19,949 次观看 • 3 个月前

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166,508 次观看 • 1 年前

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137,034 次观看 • 2 年前

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LangChain: Chat with Your Data, a new free short course created with Harrison Chase, is now available! In this 1 hour course, you’ll learn how to build one of the most requested LLM-based applications: Answering questions using information from a document or collection of documents (often called Retrieval Augmented Generation). You'll also learn how to use vector stores and embeddings to retrieve document chunks relevant to a query. I hope you enjoy the course!

Andrew Ng

384,233 次观看 • 3 年前

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Chris Long

20,891 次观看 • 2 年前

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Santiago

51,012 次观看 • 1 年前

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. If you are a Python developer, there is an open-source alternative! Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. Quix Streams is all Python, and it's open-source! The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. Here is what the video covers: • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) • How to efficiently stream that data into Kafka • How to optimize Kafka producer settings like batch size and compression for maximum throughput A few other notes about Quix Streams: • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. Thanks to the Quix team for collaborating with me on this post.

Building real-time data pipelines and stream processing is one of the best-paid skills in the market. Apache Kafka and Flink are the industry standards, but they are Java-based and have a Python wrapper. If you are a Python developer, there is an open-source alternative! Watch this video. It's a full tutorial solving a real-world problem using the Quix Streams library. Quix Streams is all Python, and it's open-source! The video shows you how to process GitHub's Firehose API, a constant stream of raw activity. You can use this stream to identify real-time trends with code, issues, and the popularity of public repositories. Here is what the video covers: • How to tap into the GitHub Firehose API using Python and server-sent events (SSE) • How to efficiently stream that data into Kafka • How to optimize Kafka producer settings like batch size and compression for maximum throughput A few other notes about Quix Streams: • You don't need to know any Java • It has a dead-simple API • It integrates seamlessly with your Python stack • It's designed for parallel processing at high velocity You can use Quix Streams to build complete stream processing pipelines in Python, including feature engineering, pre-computations, inference, and real-time machine learning. Thanks to the Quix team for collaborating with me on this post.

Santiago

89,230 次观看 • 1 年前

Claude Code just got an update nobody is talking about. And it replaced my entire video crew. You type one simple sentence. Claude writes the script. It uses ElevenLabs to clone your voice. It calls Remotion to build the scenes. It grabs HeyGen to make your avatar talk. Ten minutes later, you have a finished video. You never even turn on a camera. This changes how we make content forever.

Julian Goldie SEO

147,795 次观看 • 1 个月前