# Dishwasher

## Regex <a href="#regex" id="regex"></a>

Regex (Regular Expressions) is the most straightforward method. Entities are defined using specific patterns, and the function searches the text for matches. Regex is reliable when the input follows expected formats, but it may be less effective when handling misspellings or more complex structures.

## Email <a href="#email" id="email"></a>

Pattern: `[a-zåäöA-ZÅÄÖ0-9]+[\._]?[a-zåäöA-ZÅÄÖ0-9]+[@]\w+[.]\w{2,3}`

**Examples (matches):**

✅ <user@example.com>

✅ <user.name+alias@domain.se>

✅ någon\@exempel.åäö

✅ [me@sub.example.com ](mailto:me@sub.example.com)**Examples (non-matches):**

❌ @example.com

❌ <user@.com>

❌ user\@examp

❌ user\@example..com

## Social number <a href="#social-number" id="social-number"></a>

Pattern: `(19|20)([0-9]{4,6})([-+]|\s)?([0-9]{4})|([0-9]{2})([0-1][0-9][0-3][0-9])([-+]|\s)?([0-9]{4})`

**Examples (matches):**

✅ 19900101-1234

✅ 199001011234

✅ 900101-1234

✅ 9001011234

✅ 18991231+5678

## **Credit card number** <a href="#credit-card-number" id="credit-card-number"></a>

Pattern: `(4\d{3}|5[1-5]\d{2}|6011)(-?\s?)(\d{4})(-?\s?)(\d{4})(-?\s?)(\d{4}|3[4,7]\d{13})`

| Card type        | Begins with | Digits | Example             |
| ---------------- | ----------- | ------ | ------------------- |
| Visa             | 4xxx        | 16     | 4123-5678-9012-3456 |
| Mastercard       | 51-55xx     | 16     | 5214-5678-9012-3456 |
| Discovery        | 6011        | 16     | 6011-5678-9012-3456 |
| American Express | 34,37       | 15     | 371234567890123     |

***

## Multilingual anonymiser model  <a href="#multilingual-anonymiser-model-ai4privacy-llama-ai4privacy-multilingual-categorical-anonymiser-openpi" id="multilingual-anonymiser-model-ai4privacy-llama-ai4privacy-multilingual-categorical-anonymiser-openpi"></a>

🔗[ai4privacy/llama-ai4privacy-multilingual-categorical-anonymiser-openpii · Hugging Face](https://huggingface.co/ai4privacy/llama-ai4privacy-multilingual-categorical-anonymiser-openpii)

This is a bidirectional, encoder-only Transformer model trained to detect and redact personally identifiable information (PII) from multilingual text. It goes beyond simple pattern matching and can identify entities in more complex or unstructured formats.

Entities detected by this model include:

* AGE
* BUILDINGNUM
* CITY
* CREDITCARDNUMBER
* DATE
* DRIVERLICENSENUM
* EMAIL
* GENDER
* GIVENNAME
* IDCARDNUMBER
* PASSPORTNUM
* SEX
* SOCIALNUM
* STREET
* SURNAME
* TAXNUM
* TELEPHONENUM
* TIME
* TITLE
* ZIPCODE

## Swedish anonymiser model <a href="#swedish-anonymiser-model" id="swedish-anonymiser-model"></a>

🔗 [RecordedFuture/Swedish-NER · Hugging Face](https://huggingface.co/RecordedFuture/Swedish-NER)

This model is similar to the Multilingual anonymiser but specifically trained on Swedish data.

Entities detected by this model include:

* LOCATION
* ORGANIZATION
* PERSON
* RELIGION
* TITLE


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.ebbot.ai/ebbot-docs/core-capabilities/chat/dishwasher.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.