Einstein Platform Services

What is Einstein NER (Beta)?

NER stands for named entity recognition. The NER model takes unstructured text data and identifies entities within that text.

Note

As a beta feature, Einstein NER is a preview and isn’t part of the “Services” under your master subscription agreement with Salesforce. Use this feature at your sole discretion, and make your purchase decisions only on the basis of generally available products and features. Salesforce doesn’t guarantee general availability of this feature within any particular time frame or at all, and we can discontinue it at any time. This feature is for evaluation purposes only, not for production use. It’s offered as is and isn’t supported, and Salesforce has no liability for any harm or damage arising out of or in connection with it. All restrictions, Salesforce reservation of rights, obligations concerning the Services, and terms for related Non-Salesforce Applications and Content apply equally to your use of this feature.

If you send in this string:

Marc Benioff, the CEO of Salesforce, gave the keynote speech at the conference in Paris last week.

The model returns a prediction result like this JSON.

{
  "probabilities": [
    {
      "probability": 0.99962294,
      "label": "PERSON",
      "token": "Marc Benioff",
      "span": {
        "start": 0,
        "end": 12
      },
      "normalizedData": [
        {
          "firstName": "Marc",
          "lastName": "Benioff",
          "middleName": "",
          "suffix": "",
          "salutation": "",
          "type": "person"
        }
      ]
    },
    {
      "probability": 0.9115802,
      "label": "ORGANIZATION",
      "token": "Salesforce",
      "span": {
        "start": 27,
        "end": 37
      },
      "normalizedData": []
    },
    {
      "probability": 0.9537982,
      "label": "DATETIME",
      "token": "20 years ago",
      "span": {
        "start": 62,
        "end": 74
      },
      "normalizedData": [
        {
          "value": "2000-08-01T00:00:00.000-07:00",
          "unit": "month",
          "type": "value"
        }
      ]
    }
  ],
  "object": "predictresponse"
}

The model identifies a sequence of words (the token) that are categorized as a particular entity. The model returns the type of entity it identifies and the probability that the token is that entity.

For example, the words "Marc Benioff" are the token and the entity returned by the model is PERSON. In this case, the model identifies four entities in the text:

  • a person (Marc Benioff)
  • an organization (Salesforce)
  • a location (Paris)
  • a date/time (last week)

Entities Supported

Here are the entities that the NER model identifies.

  • DATETIME
  • DURATION
  • EMAIL
  • LOCATION
  • MONEY
  • NUMBER
  • ORGANIZATION
  • PERCENT
  • PERSON
  • PHONE-NUMBER
  • URL

For more information about the NER endpoint, see Detect Entities in Text.

Updated about a year ago

What is Einstein NER (Beta)?


NER stands for named entity recognition. The NER model takes unstructured text data and identifies entities within that text.

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