> For the complete documentation index, see [llms.txt](https://cortex-docs.paloaltonetworks.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://cortex-docs.paloaltonetworks.com/xsoar-6-administrator-guide/6.12/configure-cortex-xsoar/customize-and-configure-cortex-xsoar/machine-learning/train-a-phishing-classifier-on-non-english-languages.md). # Train a Phishing Classifier on Non-English Languages Cortex XSOAR contains out-of-the-box automations and playbooks, which are used to train phishing classifiers. By default, classifiers are configured to support phishing incidents containing English emails only. You can train a phishing classifier using emails in other languages through tokenization methods. Cortex XSOAR supports adjusted tokenization for the following languages: * German * French * Spanish * Portuguese * Italian * Dutch Although you need to train the classifier, you do not need to configure tokenization. For more information on how to configure these languages see [Train a Classifier on Languages with Adjusted Tokenization](#UUID-93c6a46f-57b9-a200-2a8e-2660246698c8). For all other languages, the language and tokenization method needs to be configured. For more information see [Train a Classifier on Other Languages](#UUID-54635d10-84c2-6a39-090d-6eea16bf91a7). ### **Train a Classifier on Languages with Adjusted Tokenization** Cortex XSOAR allows you to customize automations and playbooks to support phishing classifiers for languages other than English. Cortex XSOAR offers adjusted tokenization for the following languages: * German * French * Spanish * Portuguese * Italian * Dutch You need to configure the following automations and playbooks: * **`DBotPreProcessTextData`** * **`WordTokenizerNLP`** * **`DBotPredictPhishingWords`** * **`DBot Create Phishing Classifier V2`** 1. Go to **Automation**. 2. Configure the language for **`DBotPreProcessTextData`**. 1. Copy the **`DBotPreProcessTextData`** automation, by selecting **Duplicate Automation**. 2. (Optional) Change the name of the duplicated automation to make it distinguishable. 3. From the **Advanced** section, in the **Docker image name** field, type **`demisto/dl:languages1.0`**. 4. In the **Arguments** section, expand the **language** argument. 5. In the **Initial value** field, change the language to train the classifier. 6. Click **Save Version**. 3. Configure the language for **`WordTokenizerNLP`**. 1. Copy the **`WordTokenizerNLP`** automation, by selecting the **Duplicate Automation**. 2. (Optional) Change the name of the duplicated automation to make it distinguishable. 3. From the **Advanced** section, in the **Docker image name** field, type **`demisto/dl:languages1.0`**. 4. In the **Arguments** section, expand the **language** argument. 5. In the **Initial value** field, change the language to train the classifier. 6. Click **Save Version**. 4. Configure the language for **`DBotPredictPhishingWords`**. 1. Copy the **`DBotPredictPhishingWords`** automation by selecting **Duplicate Automation**. 2. (Optional) Change the name of the duplicated automation to make it distinguishable. 3. From the **Advanced** section, in the **Docker image name** field, type **`demisto/dl:languages1.0`**. 4. In the **Arguments** section, expand the **language** argument. 5. In the **Initial value** field change the language to train the classifier. 6. Click **Save Version**. 5. Go to **Playbooks**. 6. Search for **`DBot Create Phishing Classifier V2`** to update the playbook. 1. Copy the playbook, by selecting **Duplicate Playbook**. 2. Select the **Pre-process file** task. 3. From the dropdown menu, replace the automation with the duplicated version of **DBotPreProcessTextData** created in [Step 2](#UUID-93c6a46f-57b9-a200-2a8e-2660246698c8_id163180b7-d754-4dba-86bb-1cf146bb35d4). 4. Click **OK** and **Save Version**. ### **Train a Classifier on Other Languages** To train a classifier on languages other than those referred to in [Train a Classifier on Languages with Adjusted Tokenization](#UUID-93c6a46f-57b9-a200-2a8e-2660246698c8), you need to configure the language and tokenization method. Tokenization is the method by which the classifier breaks up sentences and words to analyze threats appropriately. When the language for the classifier is configured to **`Other`**, the user can configure the method of tokenization by which to train a classifier on to one of the following options: * Tokenization - (Default) automatically separate sentences by words * Word - separates the text based on spacing * Letter - separates the text based on charachters and symbols Follow the steps below to adjust the language and tokenization method by which to train a classifier on for other languages. 1. Go to **Automation**. 2. Search for **`DBotPreProcessTextData`**. 1. Copy the automation by selecting **Duplicate Automation**. 2. (Optional) Change the name of the duplicated script to make it distinguishable. 3. From the **Argument** section, expand the **tokenizationMethod** field, and change the **Initial value** to the desired tokenization method. For example, **`byWord`**. 4. Expand the **language** field and change the **Initial value** to **`Other`**. 5. Click **Save**. 3. Search for **`DBotPredictPhishingWords`**. 1. Copy the automation, by selecting **Duplicate Automation**. 2. (Optional) Change the name of the duplicated script to make it distinguishable. 3. From the **Argument** section, expand the **tokenizationMethod** field, and change the **Initial value** to the desired tokenization method. For example, **`byWord`**. 4. Expand the **language** field, and change the value to **`Other`**. 5. Click **Save**. 4. Navigate to **Playbooks**. 5. Search for the **`DBot Create Phishing Classifier V2`** playbook to update. 1. Copy the playbook by selecting **Duplicate Playbook**. 2. (Optional) Change the name of the duplicated playbook to make it distinguishable. 3. Select the **Pre-process file** task. 4. From the dropdown menu replace the automation with the duplicated version of **DBotPreProcessTextData** created in step 2. 5. Click **OK** and **Save Version**. --- # Agent Instructions This documentation is published with GitBook. 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