GET https://dev.normadoc.fr/_partial/products/cross-sell?limit=2&products=%5B12906%5D&template=%40SyliusShop%2FProduct%2FsimilarList.html.twig

Response
200 OK
IP
216.73.216.116
Profiled on
Token
75ef95

Forms

  • sylius_add_to_cart
    • cartItem
    • _token

sylius_add_to_cart

Form type: 
"Sylius\Bundle\CoreBundle\Form\Type\Order\AddToCartType"

Errors

This form has no errors.

Default Data

Property Value
Model Format same as normalized format
Normalized Format 
Sylius\Bundle\OrderBundle\Controller\AddToCartCommand {#106819
  -cart: App\Entity\Order\Order {#13364 …}
  -cartItem: App\Entity\Order\OrderItem {#106807
    #id: null
    #order: null
    #quantity: 1
    #unitPrice: 0
    #originalUnitPrice: 0
    #total: 0
    #immutable: false
    #units: Doctrine\Common\Collections\ArrayCollection {#106831 …}
    #unitsTotal: 0
    #adjustments: Doctrine\Common\Collections\ArrayCollection {#106832 …}
    #adjustmentsTotal: 0
    #version: 1
    #variant: App\Entity\Product\ProductVariant {#8103
      #id: 9009
      #code: "IEEE00010923PDF"
      #product: App\Entity\Product\Product {#7310
        #id: 12906
        #code: "IEEE00010923"
        #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
        #variants: Doctrine\ORM\PersistentCollection {#7743 …}
        #options: Doctrine\ORM\PersistentCollection {#7915
          #collection: Doctrine\Common\Collections\ArrayCollection {#7917 …}
          #initialized: true
          -snapshot: [ …4]
          -owner: App\Entity\Product\Product {#7310}
          -association: [ …21]
          -em: ContainerHAOxQ06\EntityManagerGhostEbeb667 {#775 …}
          -backRefFieldName: null
          -typeClass: Symfony\Component\VarDumper\Caster\CutStub {#207971 …}
          -isDirty: false
        }
        #associations: Doctrine\ORM\PersistentCollection {#7899 …}
        #createdAt: DateTime @1751040736 {#7274
          date: 2025-06-27 18:12:16.0 Europe/Paris (+02:00)
        }
        #updatedAt: DateTime @1754608621 {#7322
          date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
        }
        #enabled: true
        #translations: Doctrine\ORM\PersistentCollection {#7921 …}
        #translationsCache: [
          "en_US" => App\Entity\Product\ProductTranslation {#7920
            #locale: "en_US"
            #translatable: App\Entity\Product\Product {#7310}
            #id: 46633
            #name: "IEEE 3168:2024"
            #slug: "ieee-3168-2024-ieee00010923-244564"
            #description: """
              New IEEE Standard - Active - Draft.<br />\n
              The Natural Language Processing (NLP) services using machine learning have rich applications in solving various tasks, and have been widely deployed and used, usually accessible by API calls. The robustness of the NLP services is challenged by various well-known general corruptions and adversarial attacks. Examples of general corruptions include inadvertent or random deletion, addition, or repetition of characters or words. Adversarial attacks generate adversarial characters, words or sentence samples causing the models underpinning the NLP services to produce incorrect results. This standard proposes a method for quantitatively evaluating the robustness the NLP services. Under the method, different cases the evaluation needs to perform against are specified. Robustness metrics and their calculation are defined. With the standard, the service stakeholders including the service developer, service providers, and service users can develop understanding of the robustness of the services. The evaluation can be performed during various phases in the life cycle of the NLP services, the testing phase, in the validation phase, after deployment, etc.<br />\n
              \t\t\t\t<br />\n
              This standard specifies test methods for evaluating the robustness of a Natural Language Processing (NLP) service that uses machine learning.<br />\n
              Models of NLP generally feature an input space being discrete and an output space being almost infinite in some tasks. The robustness of the NLP service is affected by various perturbations including adversarial attacks. A methodology to categorize the perturbations, and test cases for evaluating the robustness of an NLP service against different perturbation categories is specified. Metrics for robustness evaluation of an NLP service are defined. NLP use cases and corresponding applicable test methods are also described.<br />\n
              The purpose of the standard is to provide test methods for evaluating the robustness of an NLP service. Test methods are used by service developers, service providers and service users to determine the robustness of an NLP service.
              """
            #metaKeywords: null
            #metaDescription: null
            #shortDescription: "IEEE Approved Draft Standard for Robustness Evaluation Test Methods for a Natural Language Processing Service that uses Machine Learning"
            -notes: "Active"
          }
        ]
        #currentLocale: "en_US"
        #currentTranslation: null
        #fallbackLocale: "en_US"
        #variantSelectionMethod: "match"
        #productTaxons: Doctrine\ORM\PersistentCollection {#7533 …}
        #channels: Doctrine\ORM\PersistentCollection {#7627 …}
        #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7311 …}
        #reviews: Doctrine\ORM\PersistentCollection {#7612 …}
        #averageRating: 0.0
        #images: Doctrine\ORM\PersistentCollection {#7644 …}
        -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7325 …}
        -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7389 …}
        -apiLastModifiedAt: DateTime @1754517600 {#7317
          date: 2025-08-07 00:00:00.0 Europe/Paris (+02:00)
        }
        -lastUpdatedAt: DateTime @1726092000 {#7292
          date: 2024-09-12 00:00:00.0 Europe/Paris (+02:00)
        }
        -author: ""
        -publishedAt: DateTime @1723154400 {#7318
          date: 2024-08-09 00:00:00.0 Europe/Paris (+02:00)
        }
        -releasedAt: null
        -confirmedAt: null
        -canceledAt: null
        -edition: null
        -coreDocument: "3168"
        -bookCollection: ""
        -pageCount: 29
        -documents: Doctrine\ORM\PersistentCollection {#7464 …}
        -favorites: Doctrine\ORM\PersistentCollection {#7499 …}
      }
      #optionValues: Doctrine\ORM\PersistentCollection {#8319 …}
      #position: 0
      #createdAt: DateTime @1751041383 {#7283
        date: 2025-06-27 18:23:03.0 Europe/Paris (+02:00)
      }
      #updatedAt: DateTime @1757772183 {#8120
        date: 2025-09-13 16:03:03.0 Europe/Paris (+02:00)
      }
      #enabled: true
      #translations: Doctrine\ORM\PersistentCollection {#8263 …}
      #translationsCache: [
        "en_US" => App\Entity\Product\ProductVariantTranslation {#93230
          #locale: "en_US"
          #translatable: App\Entity\Product\ProductVariant {#8103}
          #id: 9114
          #name: null
          -shortDescription: null
          -description: null
          -notes: null
          -shippingInformation: "Instant download"
        }
      ]
      #currentLocale: "en_US"
      #currentTranslation: null
      #fallbackLocale: "en_US"
      #version: 11
      #onHold: 0
      #onHand: 0
      #tracked: false
      #weight: 0.0
      #width: null
      #height: null
      #depth: null
      #taxCategory: Proxies\__CG__\App\Entity\Taxation\TaxCategory {#8135 …}
      #shippingCategory: null
      #channelPricings: Doctrine\ORM\PersistentCollection {#8297 …}
      #shippingRequired: true
      #images: Doctrine\ORM\PersistentCollection {#8294 …}
      -apiLastModifiedAt: DateTime @1757714400 {#8102
        date: 2025-09-13 00:00:00.0 Europe/Paris (+02:00)
      }
      -publishedAt: null
      -isbn: "979-8-8557-0910-0"
      -ean: "9798855709100"
      -numberOfUsers: 1
      -physicalProduct: false
      -downloadableImmediately: true
      -downloadable: true
      -drmViewerUrl: null
      -sellable: true
      -documents: Doctrine\ORM\PersistentCollection {#8131 …}
      -drmTokens: Doctrine\ORM\PersistentCollection {#8123 …}
      -enabledForSubscribers: false
      -currentAreaContext: null
    }
    #productName: null
    #variantName: null
  }
}
View Format same as normalized format

Submitted Data

This form was not submitted.

Passed Options

Option Passed Value Resolved Value
data 
Sylius\Bundle\OrderBundle\Controller\AddToCartCommand {#106819
  -cart: App\Entity\Order\Order {#13364 …}
  -cartItem: App\Entity\Order\OrderItem {#106807
    #id: null
    #order: null
    #quantity: 1
    #unitPrice: 0
    #originalUnitPrice: 0
    #total: 0
    #immutable: false
    #units: Doctrine\Common\Collections\ArrayCollection {#106831 …}
    #unitsTotal: 0
    #adjustments: Doctrine\Common\Collections\ArrayCollection {#106832 …}
    #adjustmentsTotal: 0
    #version: 1
    #variant: App\Entity\Product\ProductVariant {#8103
      #id: 9009
      #code: "IEEE00010923PDF"
      #product: App\Entity\Product\Product {#7310
        #id: 12906
        #code: "IEEE00010923"
        #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
        #variants: Doctrine\ORM\PersistentCollection {#7743 …}
        #options: Doctrine\ORM\PersistentCollection {#7915
          #collection: Doctrine\Common\Collections\ArrayCollection {#7917 …}
          #initialized: true
          -snapshot: [ …4]
          -owner: App\Entity\Product\Product {#7310}
          -association: [ …21]
          -em: ContainerHAOxQ06\EntityManagerGhostEbeb667 {#775 …}
          -backRefFieldName: null
          -typeClass: Symfony\Component\VarDumper\Caster\CutStub {#207971 …}
          -isDirty: false
        }
        #associations: Doctrine\ORM\PersistentCollection {#7899 …}
        #createdAt: DateTime @1751040736 {#7274
          date: 2025-06-27 18:12:16.0 Europe/Paris (+02:00)
        }
        #updatedAt: DateTime @1754608621 {#7322
          date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
        }
        #enabled: true
        #translations: Doctrine\ORM\PersistentCollection {#7921 …}
        #translationsCache: [
          "en_US" => App\Entity\Product\ProductTranslation {#7920
            #locale: "en_US"
            #translatable: App\Entity\Product\Product {#7310}
            #id: 46633
            #name: "IEEE 3168:2024"
            #slug: "ieee-3168-2024-ieee00010923-244564"
            #description: """
              New IEEE Standard - Active - Draft.<br />\n
              The Natural Language Processing (NLP) services using machine learning have rich applications in solving various tasks, and have been widely deployed and used, usually accessible by API calls. The robustness of the NLP services is challenged by various well-known general corruptions and adversarial attacks. Examples of general corruptions include inadvertent or random deletion, addition, or repetition of characters or words. Adversarial attacks generate adversarial characters, words or sentence samples causing the models underpinning the NLP services to produce incorrect results. This standard proposes a method for quantitatively evaluating the robustness the NLP services. Under the method, different cases the evaluation needs to perform against are specified. Robustness metrics and their calculation are defined. With the standard, the service stakeholders including the service developer, service providers, and service users can develop understanding of the robustness of the services. The evaluation can be performed during various phases in the life cycle of the NLP services, the testing phase, in the validation phase, after deployment, etc.<br />\n
              \t\t\t\t<br />\n
              This standard specifies test methods for evaluating the robustness of a Natural Language Processing (NLP) service that uses machine learning.<br />\n
              Models of NLP generally feature an input space being discrete and an output space being almost infinite in some tasks. The robustness of the NLP service is affected by various perturbations including adversarial attacks. A methodology to categorize the perturbations, and test cases for evaluating the robustness of an NLP service against different perturbation categories is specified. Metrics for robustness evaluation of an NLP service are defined. NLP use cases and corresponding applicable test methods are also described.<br />\n
              The purpose of the standard is to provide test methods for evaluating the robustness of an NLP service. Test methods are used by service developers, service providers and service users to determine the robustness of an NLP service.
              """
            #metaKeywords: null
            #metaDescription: null
            #shortDescription: "IEEE Approved Draft Standard for Robustness Evaluation Test Methods for a Natural Language Processing Service that uses Machine Learning"
            -notes: "Active"
          }
        ]
        #currentLocale: "en_US"
        #currentTranslation: null
        #fallbackLocale: "en_US"
        #variantSelectionMethod: "match"
        #productTaxons: Doctrine\ORM\PersistentCollection {#7533 …}
        #channels: Doctrine\ORM\PersistentCollection {#7627 …}
        #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7311 …}
        #reviews: Doctrine\ORM\PersistentCollection {#7612 …}
        #averageRating: 0.0
        #images: Doctrine\ORM\PersistentCollection {#7644 …}
        -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7325 …}
        -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7389 …}
        -apiLastModifiedAt: DateTime @1754517600 {#7317
          date: 2025-08-07 00:00:00.0 Europe/Paris (+02:00)
        }
        -lastUpdatedAt: DateTime @1726092000 {#7292
          date: 2024-09-12 00:00:00.0 Europe/Paris (+02:00)
        }
        -author: ""
        -publishedAt: DateTime @1723154400 {#7318
          date: 2024-08-09 00:00:00.0 Europe/Paris (+02:00)
        }
        -releasedAt: null
        -confirmedAt: null
        -canceledAt: null
        -edition: null
        -coreDocument: "3168"
        -bookCollection: ""
        -pageCount: 29
        -documents: Doctrine\ORM\PersistentCollection {#7464 …}
        -favorites: Doctrine\ORM\PersistentCollection {#7499 …}
      }
      #optionValues: Doctrine\ORM\PersistentCollection {#8319 …}
      #position: 0
      #createdAt: DateTime @1751041383 {#7283
        date: 2025-06-27 18:23:03.0 Europe/Paris (+02:00)
      }
      #updatedAt: DateTime @1757772183 {#8120
        date: 2025-09-13 16:03:03.0 Europe/Paris (+02:00)
      }
      #enabled: true
      #translations: Doctrine\ORM\PersistentCollection {#8263 …}
      #translationsCache: [
        "en_US" => App\Entity\Product\ProductVariantTranslation {#93230
          #locale: "en_US"
          #translatable: App\Entity\Product\ProductVariant {#8103}
          #id: 9114
          #name: null
          -shortDescription: null
          -description: null
          -notes: null
          -shippingInformation: "Instant download"
        }
      ]
      #currentLocale: "en_US"
      #currentTranslation: null
      #fallbackLocale: "en_US"
      #version: 11
      #onHold: 0
      #onHand: 0
      #tracked: false
      #weight: 0.0
      #width: null
      #height: null
      #depth: null
      #taxCategory: Proxies\__CG__\App\Entity\Taxation\TaxCategory {#8135 …}
      #shippingCategory: null
      #channelPricings: Doctrine\ORM\PersistentCollection {#8297 …}
      #shippingRequired: true
      #images: Doctrine\ORM\PersistentCollection {#8294 …}
      -apiLastModifiedAt: DateTime @1757714400 {#8102
        date: 2025-09-13 00:00:00.0 Europe/Paris (+02:00)
      }
      -publishedAt: null
      -isbn: "979-8-8557-0910-0"
      -ean: "9798855709100"
      -numberOfUsers: 1
      -physicalProduct: false
      -downloadableImmediately: true
      -downloadable: true
      -drmViewerUrl: null
      -sellable: true
      -documents: Doctrine\ORM\PersistentCollection {#8131 …}
      -drmTokens: Doctrine\ORM\PersistentCollection {#8123 …}
      -enabledForSubscribers: false
      -currentAreaContext: null
    }
    #productName: null
    #variantName: null
  }
}
 same as passed value
product 
App\Entity\Product\Product {#7310
  #id: 12906
  #code: "IEEE00010923"
  #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
  #variants: Doctrine\ORM\PersistentCollection {#7743 …}
  #options: Doctrine\ORM\PersistentCollection {#7915
    #collection: Doctrine\Common\Collections\ArrayCollection {#7917 …}
    #initialized: true
    -snapshot: [ …4]
    -owner: App\Entity\Product\Product {#7310}
    -association: [ …21]
    -em: ContainerHAOxQ06\EntityManagerGhostEbeb667 {#775 …}
    -backRefFieldName: null
    -typeClass: Symfony\Component\VarDumper\Caster\CutStub {#207971 …}
    -isDirty: false
  }
  #associations: Doctrine\ORM\PersistentCollection {#7899 …}
  #createdAt: DateTime @1751040736 {#7274
    date: 2025-06-27 18:12:16.0 Europe/Paris (+02:00)
  }
  #updatedAt: DateTime @1754608621 {#7322
    date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
  }
  #enabled: true
  #translations: Doctrine\ORM\PersistentCollection {#7921 …}
  #translationsCache: [
    "en_US" => App\Entity\Product\ProductTranslation {#7920
      #locale: "en_US"
      #translatable: App\Entity\Product\Product {#7310}
      #id: 46633
      #name: "IEEE 3168:2024"
      #slug: "ieee-3168-2024-ieee00010923-244564"
      #description: """
        New IEEE Standard - Active - Draft.<br />\n
        The Natural Language Processing (NLP) services using machine learning have rich applications in solving various tasks, and have been widely deployed and used, usually accessible by API calls. The robustness of the NLP services is challenged by various well-known general corruptions and adversarial attacks. Examples of general corruptions include inadvertent or random deletion, addition, or repetition of characters or words. Adversarial attacks generate adversarial characters, words or sentence samples causing the models underpinning the NLP services to produce incorrect results. This standard proposes a method for quantitatively evaluating the robustness the NLP services. Under the method, different cases the evaluation needs to perform against are specified. Robustness metrics and their calculation are defined. With the standard, the service stakeholders including the service developer, service providers, and service users can develop understanding of the robustness of the services. The evaluation can be performed during various phases in the life cycle of the NLP services, the testing phase, in the validation phase, after deployment, etc.<br />\n
        \t\t\t\t<br />\n
        This standard specifies test methods for evaluating the robustness of a Natural Language Processing (NLP) service that uses machine learning.<br />\n
        Models of NLP generally feature an input space being discrete and an output space being almost infinite in some tasks. The robustness of the NLP service is affected by various perturbations including adversarial attacks. A methodology to categorize the perturbations, and test cases for evaluating the robustness of an NLP service against different perturbation categories is specified. Metrics for robustness evaluation of an NLP service are defined. NLP use cases and corresponding applicable test methods are also described.<br />\n
        The purpose of the standard is to provide test methods for evaluating the robustness of an NLP service. Test methods are used by service developers, service providers and service users to determine the robustness of an NLP service.
        """
      #metaKeywords: null
      #metaDescription: null
      #shortDescription: "IEEE Approved Draft Standard for Robustness Evaluation Test Methods for a Natural Language Processing Service that uses Machine Learning"
      -notes: "Active"
    }
  ]
  #currentLocale: "en_US"
  #currentTranslation: null
  #fallbackLocale: "en_US"
  #variantSelectionMethod: "match"
  #productTaxons: Doctrine\ORM\PersistentCollection {#7533 …}
  #channels: Doctrine\ORM\PersistentCollection {#7627 …}
  #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7311 …}
  #reviews: Doctrine\ORM\PersistentCollection {#7612 …}
  #averageRating: 0.0
  #images: Doctrine\ORM\PersistentCollection {#7644 …}
  -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7325 …}
  -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7389 …}
  -apiLastModifiedAt: DateTime @1754517600 {#7317
    date: 2025-08-07 00:00:00.0 Europe/Paris (+02:00)
  }
  -lastUpdatedAt: DateTime @1726092000 {#7292
    date: 2024-09-12 00:00:00.0 Europe/Paris (+02:00)
  }
  -author: ""
  -publishedAt: DateTime @1723154400 {#7318
    date: 2024-08-09 00:00:00.0 Europe/Paris (+02:00)
  }
  -releasedAt: null
  -confirmedAt: null
  -canceledAt: null
  -edition: null
  -coreDocument: "3168"
  -bookCollection: ""
  -pageCount: 29
  -documents: Doctrine\ORM\PersistentCollection {#7464 …}
  -favorites: Doctrine\ORM\PersistentCollection {#7499 …}
}
 same as passed value

Resolved Options

Option Value
action 
""
allow_extra_fields 
false
allow_file_upload 
false
attr 
[]
attr_translation_parameters 
[]
auto_initialize 
true
block_name 
null
block_prefix 
null
by_reference 
true
compound 
true
constraints 
[]
csrf_field_name 
"_token"
csrf_message 
"The CSRF token is invalid. Please try to resubmit the form."
csrf_protection 
true
csrf_token_id 
null
csrf_token_manager 
Symfony\Component\Security\Csrf\CsrfTokenManager {#106839
  -generator: Symfony\Component\Security\Csrf\TokenGenerator\UriSafeTokenGenerator {#106840 …}
  -storage: Symfony\Component\Security\Csrf\TokenStorage\SessionTokenStorage {#106841 …}
  -namespace: Closure() {#106843 …}
}
data 
Sylius\Bundle\OrderBundle\Controller\AddToCartCommand {#106819
  -cart: App\Entity\Order\Order {#13364 …}
  -cartItem: App\Entity\Order\OrderItem {#106807
    #id: null
    #order: null
    #quantity: 1
    #unitPrice: 0
    #originalUnitPrice: 0
    #total: 0
    #immutable: false
    #units: Doctrine\Common\Collections\ArrayCollection {#106831 …}
    #unitsTotal: 0
    #adjustments: Doctrine\Common\Collections\ArrayCollection {#106832 …}
    #adjustmentsTotal: 0
    #version: 1
    #variant: App\Entity\Product\ProductVariant {#8103
      #id: 9009
      #code: "IEEE00010923PDF"
      #product: App\Entity\Product\Product {#7310
        #id: 12906
        #code: "IEEE00010923"
        #attributes: Doctrine\ORM\PersistentCollection {#7700 …}
        #variants: Doctrine\ORM\PersistentCollection {#7743 …}
        #options: Doctrine\ORM\PersistentCollection {#7915
          #collection: Doctrine\Common\Collections\ArrayCollection {#7917 …}
          #initialized: true
          -snapshot: [ …4]
          -owner: App\Entity\Product\Product {#7310}
          -association: [ …21]
          -em: ContainerHAOxQ06\EntityManagerGhostEbeb667 {#775 …}
          -backRefFieldName: null
          -typeClass: Symfony\Component\VarDumper\Caster\CutStub {#207971 …}
          -isDirty: false
        }
        #associations: Doctrine\ORM\PersistentCollection {#7899 …}
        #createdAt: DateTime @1751040736 {#7274
          date: 2025-06-27 18:12:16.0 Europe/Paris (+02:00)
        }
        #updatedAt: DateTime @1754608621 {#7322
          date: 2025-08-08 01:17:01.0 Europe/Paris (+02:00)
        }
        #enabled: true
        #translations: Doctrine\ORM\PersistentCollection {#7921 …}
        #translationsCache: [
          "en_US" => App\Entity\Product\ProductTranslation {#7920
            #locale: "en_US"
            #translatable: App\Entity\Product\Product {#7310}
            #id: 46633
            #name: "IEEE 3168:2024"
            #slug: "ieee-3168-2024-ieee00010923-244564"
            #description: """
              New IEEE Standard - Active - Draft.<br />\n
              The Natural Language Processing (NLP) services using machine learning have rich applications in solving various tasks, and have been widely deployed and used, usually accessible by API calls. The robustness of the NLP services is challenged by various well-known general corruptions and adversarial attacks. Examples of general corruptions include inadvertent or random deletion, addition, or repetition of characters or words. Adversarial attacks generate adversarial characters, words or sentence samples causing the models underpinning the NLP services to produce incorrect results. This standard proposes a method for quantitatively evaluating the robustness the NLP services. Under the method, different cases the evaluation needs to perform against are specified. Robustness metrics and their calculation are defined. With the standard, the service stakeholders including the service developer, service providers, and service users can develop understanding of the robustness of the services. The evaluation can be performed during various phases in the life cycle of the NLP services, the testing phase, in the validation phase, after deployment, etc.<br />\n
              \t\t\t\t<br />\n
              This standard specifies test methods for evaluating the robustness of a Natural Language Processing (NLP) service that uses machine learning.<br />\n
              Models of NLP generally feature an input space being discrete and an output space being almost infinite in some tasks. The robustness of the NLP service is affected by various perturbations including adversarial attacks. A methodology to categorize the perturbations, and test cases for evaluating the robustness of an NLP service against different perturbation categories is specified. Metrics for robustness evaluation of an NLP service are defined. NLP use cases and corresponding applicable test methods are also described.<br />\n
              The purpose of the standard is to provide test methods for evaluating the robustness of an NLP service. Test methods are used by service developers, service providers and service users to determine the robustness of an NLP service.
              """
            #metaKeywords: null
            #metaDescription: null
            #shortDescription: "IEEE Approved Draft Standard for Robustness Evaluation Test Methods for a Natural Language Processing Service that uses Machine Learning"
            -notes: "Active"
          }
        ]
        #currentLocale: "en_US"
        #currentTranslation: null
        #fallbackLocale: "en_US"
        #variantSelectionMethod: "match"
        #productTaxons: Doctrine\ORM\PersistentCollection {#7533 …}
        #channels: Doctrine\ORM\PersistentCollection {#7627 …}
        #mainTaxon: Proxies\__CG__\App\Entity\Taxonomy\Taxon {#7311 …}
        #reviews: Doctrine\ORM\PersistentCollection {#7612 …}
        #averageRating: 0.0
        #images: Doctrine\ORM\PersistentCollection {#7644 …}
        -supplier: Proxies\__CG__\App\Entity\Supplier\Supplier {#7325 …}
        -subscriptionCollections: Doctrine\ORM\PersistentCollection {#7389 …}
        -apiLastModifiedAt: DateTime @1754517600 {#7317
          date: 2025-08-07 00:00:00.0 Europe/Paris (+02:00)
        }
        -lastUpdatedAt: DateTime @1726092000 {#7292
          date: 2024-09-12 00:00:00.0 Europe/Paris (+02:00)
        }
        -author: ""
        -publishedAt: DateTime @1723154400 {#7318
          date: 2024-08-09 00:00:00.0 Europe/Paris (+02:00)
        }
        -releasedAt: null
        -confirmedAt: null
        -canceledAt: null
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