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articles/active-directory/authentication/concept-fido2-hardware-vendor.md

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| [IDmelon Technologies Inc.](https://www.idmelon.com/#idmelon) | ![y] | ![y]| ![y]| ![y]| ![n] |
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| [Kensington](https://www.kensington.com/solutions/product-category/why-biometrics/) | ![y] | ![y]| ![n]| ![n]| ![n] |
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| [KONA I](https://konai.com/business/security/fido) | ![y] | ![n]| ![y]| ![y]| ![n] |
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| [Movenda](https://www.movenda.com/en/authentication/fido2/overview) | ![y] | ![n]| ![y]| ![y]| ![n] |
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| [NeoWave](https://neowave.fr/en/products/fido-range/) | ![n] | ![y]| ![y]| ![n]| ![n] |
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| [Nymi](https://www.nymi.com/nymi-band) | ![y] | ![n]| ![y]| ![n]| ![n] |
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| [Octatco](https://octatco.com/) | ![y] | ![y]| ![n]| ![n]| ![n] |

articles/active-directory/saas-apps/successfactors-tutorial.md

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- `https://<companyname>.sapsf.cn/<companyname>`
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> [!NOTE]
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> These values are not real. Update these values with the actual Sign-on URL, Identifier and Reply URL. Contact [SuccessFactors Client support team](https://www.sap.com/support.html) to get these values.
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> These values are not real. Update these values with the actual Sign-on URL, Identifier and Reply URL. Contact [SuccessFactors Client support team](https://www.sap.com/services-support.html) to get these values.
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4. On the **Set up Single Sign-On with SAML** page, in the **SAML Signing Certificate** section, find **Certificate (Base64)** and select **Download** to download the certificate and save it on your computer.
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To enable Azure AD users to sign in to SuccessFactors, they must be provisioned into SuccessFactors. In the case of SuccessFactors, provisioning is a manual task.
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To get users created in SuccessFactors, you need to contact the [SuccessFactors support team](https://www.sap.com/support.html).
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To get users created in SuccessFactors, you need to contact the [SuccessFactors support team](https://www.sap.com/services-support.html).
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## Test SSO
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articles/ai-services/document-intelligence/concept-custom-neural.md

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[!INCLUDE [applies to v3.1 and v3.0](includes/applies-to-v3-1-v3-0.md)]
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Custom neural document models or neural models are a deep learned model type that combines layout and language features to accurately extract labeled fields from documents. The base custom neural model is trained on various document types that makes it suitable to be trained for extracting fields from structured, semi-structured and unstructured documents. The table below lists common document types for each category:
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|Documents | Examples |
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## Supported languages and locales
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1. Neural models now support added languages in the ```v3.1 and v3.0``` APIs.
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>[!NOTE]
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> Document Intelligence auto-detects language and locale data.
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:::moniker range="doc-intel-3.1.0"
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Neural models now support added languages for the ```v3.1``` APIs.
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|Language| Code (optional) |
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|:-----|:----:|
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|Afrikaans| `af`|
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|Albanian| `sq`|
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|Arabic|`ar`|
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|Bulgarian|`bg`|
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|Chinese (Han (Simplified variant))| `zh-Hans`|
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|Chinese (Han (Traditional variant))|`zh-Hant`|
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|Croatian|`hr`|
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|Czech|`cs`|
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|Danish|`da`|
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|Dutch|`nl`|
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|Estonian|`et`|
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|Finnish|`fi`|
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|French|`fr`|
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|German|`de`|
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|Hebrew|`he`|
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|Hindi|`hi`|
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|Hungarian|`hu`|
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|Indonesian|`id`|
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|Italian|`it`|
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|Japanese|`ja`|
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|Korean|`ko`|
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|Latvian|`lv`|
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|Lithuanian|`lt`|
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|Macedonian|`mk`|
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|Marathi|`mr`|
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|Modern Greek (1453-)|`el`|
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|Nepali (macrolanguage)|`ne`|
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|Norwegian|`no`|
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|Panjabi|`pa`|
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|Persian|`fa`|
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|Polish|`pl`|
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|Portuguese|`pt`|
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|Romanian|`rm`|
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|Russian|`ru`|
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|Slovak|`sk`|
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|Slovenian|`sl`|
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|Somali (Arabic)|`so`|
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|Somali (Latin)|`so-latn`|
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|Spanish|`es`|
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|Swahili (macrolanguage)|`sw`|
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|Swedish|`sv`|
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|Tamil|`ta`|
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|Thai|`th`|
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|Turkish|`tr`|
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|Ukrainian|`uk`|
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|Urdu|`ur`|
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|Vietnamese|`vi`|
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:::moniker-end
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:::moniker range="doc-intel-3.0.0"
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Neural models now support added languages for the ```v3.0``` APIs.
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| Languages | API version |
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| Spanish | `2023-07-31` (GA)|
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| Dutch | `2023-07-31` (GA)|
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:::moniker-end
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## Tabular fields
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With the release of API versions **2022-06-30-preview** and later, custom neural models will support tabular fields (tables):

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