ערוך

שתף באמצעות

Data formats accepted by conversational language understanding

  • מאמר

If you're uploading your data into conversational language understanding, it must follow a specific format. Use this article to learn more about accepted data formats.

Import project file format

If you're importing a project into conversational language understanding, the file uploaded must be in the following format:

{
  "projectFileVersion": "2022-10-01-preview",
  "stringIndexType": "Utf16CodeUnit",
  "metadata": {
    "projectKind": "Conversation",
    "projectName": "{PROJECT-NAME}",
    "multilingual": true,
    "description": "DESCRIPTION",
    "language": "{LANGUAGE-CODE}",
    "settings": {
            "confidenceThreshold": 0
        }
  },
  "assets": {
    "projectKind": "Conversation",
    "intents": [
      {
        "category": "intent1"
      }
    ],
    "entities": [
      {
        "category": "entity1",
        "compositionSetting": "{COMPOSITION-SETTING}",
        "list": {
          "sublists": [
            {
              "listKey": "list1",
              "synonyms": [
                {
                  "language": "{LANGUAGE-CODE}",
                  "values": [
                    "{VALUES-FOR-LIST}"
                  ]
                }
              ]
            }            
          ]
        },
        "prebuilts": [
          {
            "category": "{PREBUILT-COMPONENTS}"
          }
        ],
        "regex": {
          "expressions": [
              {
                  "regexKey": "regex1",
                  "language": "{LANGUAGE-CODE}",
                  "regexPattern": "{REGEX-PATTERN}"
              }
          ]
        },
        "requiredComponents": [
            "{REQUIRED-COMPONENTS}"
        ]
      }
    ],
    "utterances": [
      {
        "text": "utterance1",
        "intent": "intent1",
        "language": "{LANGUAGE-CODE}",
        "dataset": "{DATASET}",
        "entities": [
          {
            "category": "ENTITY1",
            "offset": 6,
            "length": 4
          }
        ]
      }
    ]
  }
}
Key Placeholder Value Example
{API-VERSION} The version of the API you're calling. 2023-04-01
confidenceThreshold {CONFIDENCE-THRESHOLD} This is the threshold score below which the intent is predicted as None intent. Values are from 0 to 1. 0.7
projectName {PROJECT-NAME} The name of your project. This value is case sensitive. EmailApp
multilingual true A Boolean value that enables you to have utterances in multiple languages in your dataset. When your model is deployed, you can query the model in any supported language (not necessarily included in your training documents. For more information about supported language codes, see Language support. true
sublists [] Array that contains sublists. Each sublist is a key and its associated values. []
compositionSetting {COMPOSITION-SETTING} Rule that defines how to manage multiple components in your entity. Options are combineComponents or separateComponents. combineComponents
synonyms [] Array that contains all the synonyms. synonym
language {LANGUAGE-CODE} A string specifying the language code for the utterances, synonyms, and regular expressions used in your project. If your project is a multilingual project, choose the language code of most the utterances. en-us
intents [] Array that contains all the intents you have in the project. These intents are classified from your utterances. []
entities [] Array that contains all the entities in your project. These entities are extracted from your utterances. Every entity can have other optional components defined with them: list, prebuilt, or regex. []
dataset {DATASET} The test set to which this utterance goes to when it's split before training. To learn more about data splitting, see Train your conversational language understanding model. Possible values for this field are Train and Test. Train
category The type of entity associated with the span of text specified. Entity1
offset The inclusive character position of the start of the entity. 5
length The character length of the entity. 5
listKey A normalized value for the list of synonyms to map back to in prediction. Microsoft
values {VALUES-FOR-LIST} A list of comma-separated strings that are matched exactly for extraction and map to the list key. "msft", "microsoft", "MS"
regexKey {REGEX-PATTERN} A normalized value for the regular expression to map back to in prediction. ProductPattern1
regexPattern {REGEX-PATTERN} A regular expression. ^pre
prebuilts {PREBUILT-COMPONENTS} The prebuilt components that can extract common types. For the list of prebuilts you can add, see Supported prebuilt entity components. Quantity.Number
requiredComponents {REQUIRED-COMPONENTS} A setting that specifies a requirement that a specific component must be present to return the entity. To learn more, see Entity components. The possible values are learned, regex, list, or prebuilts. "learned", "prebuilt"

Utterance file format

Conversational language understanding offers the option to upload your utterances directly to the project rather than typing them in one by one. You can find this option on the data labeling page for your project.

[
    {
        "text": "{Utterance-Text}",
        "language": "{LANGUAGE-CODE}",
        "dataset": "{DATASET}",
        "intent": "{intent}",
        "entities": [
            {
                "category": "{entity}",
                "offset": 19,
                "length": 10
            }
        ]
    },
    {
        "text": "{Utterance-Text}",
        "language": "{LANGUAGE-CODE}",
        "dataset": "{DATASET}",
        "intent": "{intent}",
        "entities": [
            {
                "category": "{entity}",
                "offset": 20,
                "length": 10
            },
            {
                "category": "{entity}",
                "offset": 31,
                "length": 5
            }
        ]
    }
]
Key Placeholder Value Example
text {Utterance-Text} Your utterance text. Testing
language {LANGUAGE-CODE} A string that specifies the language code for the utterances used in your project. If your project is a multilingual project, choose the language code of most of the utterances. For more information about supported language codes, see Language support. en-us
dataset {DATASET} The test set to which this utterance goes to when it's split before training. To learn more about data splitting, see Train your conversational language understanding model. Possible values for this field are Train and Test. Train
intent {intent} The assigned intent. intent1
entity {entity} The entity to be extracted. entity1
category The type of entity associated with the span of text specified. Entity1
offset The inclusive character position of the start of the text. 0
length The length of the bounding box in terms of UTF16 characters. Training only considers the data in this region. 500

Related content