BERT relevance score Tool
Update → You can now use the Bert score relevance checker for both URL and Content input types
Google uses BERT to improve search rankings and featured snippets, helping users find more useful and relevant information.
The BERT Relevance Score Calculator lets you measure how well content aligns with Google’s advanced language model. Instead of just checking for exact keyword matches. This tool evaluates semantic similarity, ensuring your content is truly relevant and matches search intent (using Google’s own technology).
Are you interested in integrating this functionality into your CMS or application? Discover the BERT Score API →
How this tool works?
The BERT content relevance score calculator measures topic similarity by comparing their vector representations (embeddings) of the provided content and keyword. It does this using cosine similarity, a mathematical way to measure similarity between two vectors. In this case, the keyword acts as the reference text, and the higher the similarity, the more closely the compared text matches the keyword.
What is Google BERT?
Google BERT (Bidirectional Encoder Representations from Transformers) is a natural language processing (NLP) model developed by Google to improve the understanding of search queries. It helps Google interpret the intent and context of words in a sentence by analyzing them in relation to surrounding words rather than individually.
More information (video explanation) on how Google utilizes Google BERT as part of Google Search in DeepRank →
Bert Score interpretation
The SEO content relevance tool translates BERT similarity scores from 0-1 into a percentage-based system (0-100) for easier interpretation.
| BERT Score | Meaning |
| 100 | Perfect match |
| 99 – 80 | Very strong relevance |
| 79 – 60 | Good relevance |
| 59 – 40 | Somewhat related |
| 39 – 20 | Weak relevance (might share a few common words but different meaning) |
| 19 – 1 | Very weak or no similarity |
| 0 or below | Opposite meaning |
TIP: When the BERT score is below 50 we recommend using our SEO Content Editor → to adjust your content to help it align with your focus keyword and content topic.
Why use Google BERT?
- BERT was developed by Google
- Google developed BERT to help understand the intent behind search queries.
- Captures Meaning Beyond Exact Matches
- Traditional keyword matching relies on exact words, missing synonyms or related terms.
- Embeddings allow you to compare the context and meaning, so “running shoes” and “sneakers” can be considered similar.
- Handles Misspellings & Variations
- Unlike simple text matching, embeddings can recognize that “optimization” and “optimisation” are essentially the same concept.
- Enables Ranking by Relevances
- Instead of a binary match (yes/no), embeddings let you score how similar two texts are.
- Example: “best SEO strategies” might have a high similarity to “SEO techniques” but low similarity to “shoe sizes.”
What is the difference between BERT and other LLMs?
Unlike traditional models that read text one word at a time (left to right or right to left), BERT processes an entire sentence at once (bidirectionally). This allows it to understand words in context rather than treating them as isolated terms.
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