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Added a short explanation of the difference between zeroshot and guid… #2238

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2faf380
Added a short explanation of the difference between zeroshot and guid…
janspoerer Dec 8, 2024
f0359bb
Merge branch 'MaartenGr:master' into short-explanation-difference-bet…
janspoerer Dec 13, 2024
1462f36
Update docs/getting_started/guided/guided.md
janspoerer Mar 31, 2025
1d50389
Merge branch 'master' into short-explanation-difference-between-guide…
janspoerer Mar 31, 2025
9b050cc
Removed trailing whitespace (linter was failing)
janspoerer Apr 1, 2025
99530b5
Removed trailing whitespace to satisfy the linter
janspoerer Apr 1, 2025
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Minimal typo correction (removing trailing unnecessary "to"); mainly …
janspoerer Apr 2, 2025
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7 changes: 6 additions & 1 deletion docs/getting_started/guided/guided.md
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Guided Topic Modeling or Seeded Topic Modeling is a collection of techniques that guides the topic modeling approach by setting several seed topics to which the model will converge to. These techniques allow the user to set a predefined number of topic representations that are sure to be in documents. For example, take an IT business that has a ticket system for the software their clients use. Those tickets may typically contain information about a specific bug regarding login issues that the IT business is aware of.
!!! Note
Difference between Zero-shot and Guided BERTopic:
Guided BERTopic is similar - yet not equivalent - to [Zeros-shot Topic Modeling](https://maartengr.github.io/BERTopic/getting_started/zeroshot/zeroshot.html).
Use Guided BERTopic to boost the importance of certain keywords. Use [Zeros-shot Topic Modeling](https://maartengr.github.io/BERTopic/getting_started/zeroshot/zeroshot.html) to try to categorize documents into predefined topics ("zero-shot topics") before clustering the remaining unclassified documents using the main algorithm of BERTopic.
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I feel like we have to further specify that Guided BERTopic is not primarily used to boost the importance of certain keywords but to guide clusters more towards predefined seed topics. That would make it a bit more clear that we do not focus on the importance of certain keywords (that's more of an side-effect).


Guided Topic Modeling or Seeded Topic Modeling is a collection of techniques that guides the topic modeling approach by setting several seed topics to which the model will converge. These techniques allow the user to set a predefined number of topic representations that are sure to be in documents. For example, take an IT business that has a ticket system for the software their clients use. Those tickets may typically contain information about a specific bug regarding login issues that the IT business is aware of.

To model that bug, we can create a seed topic representation containing the words `bug`, `login`, `password`,
and `username`. By defining those words, a Guided Topic Modeling approach will try to converge at least one topic to those words.
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4 changes: 4 additions & 0 deletions docs/getting_started/zeroshot/zeroshot.md
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!!! Note
Difference between Zero-shot and Guided BERTopic:
Zeros-shot Topic Modeling is similar - yet not equivalent - to [Guided BERTopic](https://maartengr.github.io/BERTopic/getting_started/guided/guided.html). Use [Guided BERTopic](https://maartengr.github.io/BERTopic/getting_started/guided/guided.html) to boost certain keyword's importance. Use [Zeros-shot Topic Modeling](https://maartengr.github.io/BERTopic/getting_started/zeroshot/zeroshot.html) to try to categorize documents into predefined topics ("zero-shot topics") before the clustering the remaining, unclassified documents, using the default unsupervised BERTopic topic exploration algorithm.

Zero-shot Topic Modeling is a technique that allows you to find topics in large amounts of documents that were predefined. When faced with many documents, you often have an idea of which topics will definitely be in there. Whether that is a result of simply knowing your data or if a domain expert is involved in defining those topics.

This method allows you to not only find those specific topics but also create new topics for documents that would not fit with your predefined topics.
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