• Text Embedding

    This Text Embedding model can be used as the base model to any custom text classification model. Customers should use our Text Embedding model for the following use cases: topic analysis, sentiment analysis, custom text moderation, chatbot and email smart replies. Understand purchasing intent; understand audience sentiment; moderate user generated content; determine brand reputation; improve customer satisfaction and more.

    Start now for free Contact us

    5,000 free operations per month.

Request


You can call the Predict API with the "Text Embedding" model. Simply pass in a text input with a publicly accessible URL or by directly sending raw text, .csv or .tsv files. You can learn more about the Predict API in our API Guide. 

Python JS cURL
1 
// Python
model = app.models.get('d2b6d1aa64c9541784e412347f582bc0')
text = "This is a text string"
model.predict([text])

Response

The Predict API returns ‘vector’ and ‘num_dimensions’. The ‘vector’ is a numerical vector that represents the input text in a 768-dimensional space. The numerical values within the vectors are between 0 and 1, inclusive. The ‘num_dimensions’ for this model is set at 768.

Response
1 
{
  "status": {
    "code": 10000,
    "description": "Ok"
  },
  "outputs": [
    {
      "id": "d2b6d1aa64c9541784e412347f582bc0",
      "status": {
        "code": 10000,
        "description": "Ok"
      },
      "created_at": "2017-04-06T17:43:28.573076Z",
      "model": {
        "name": "general-v1.3",
        "id": "bbb5f41425b8468d9b7a554ff10f8581",
        "created_at": "2016-06-05T17:26:32.302967Z",
        "app_id": null,
        "output_info": {
          "message": "Show output_info with: GET /models/{model_id}/output_info",
          "type": "embed",
          "type_ext": "embed"
        },
        "model_version": {
          "id": "bb7ac05c86be42d38b67bc473d333e07",
          "created_at": "2016-07-13T00:58:55.915745Z",
          "status": {
            "code": 21100,
            "description": "Model trained successfully"
          }
        }
        
      },
      "input": {
        "id": "afb815ae2cc84149bce35df866ade49e",
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/metro-north.jpg"
          }
        }
      },
      "data": {
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      }
    }
  ]
}

Component

Description

Model

The model you are using to make predictions on your inputs with. Includes the id, name, created_at date, output info, and information on the model version.

Input

The text data that you passed through the model to make predictions. Includes id and the image data.

Data

The data sent back with the response; usually includes the detected concepts and corresponding probability values. (If the model's output type is not concepts, you'll see different output responses here.)

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