Text Embedding Model | Analyze Topics and Sentiment

Text Embedding

This 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.

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.

Learn more about the Predict API.
API Guide

Python JS cURL

//Python
from clarifai_grpc.grpc.api import service_pb2, service_pb2_grpc, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2
from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
import json

channel = ClarifaiChannel.get_json_channel()

stub = service_pb2_grpc.V2Stub(channel)


response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        model_id="54286fce2de6cdfa11fedb3b498d2523",
        # By default, the latest model version will be used, but you can set it explicitly.
        # version_id=model_version_id,
        inputs=[
            resources_pb2.Input(data=resources_pb2.Data(text=resources_pb2.Text(raw="Butchart Gardens contains over 900 varieties of plants."))),
            resources_pb2.Input(data=resources_pb2.Data(text=resources_pb2.Text(url="https://samples.clarifai.com/negative_sentence_12.txt"))),
        ]
    ),
    metadata=(('authorization', 'Key YOUR_API_KEY'),)
)



if response.status.code != status_code_pb2.SUCCESS:
    raise Exception("Request failed, status code: "+ str(response.status.code))


print(response)

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

{
  "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": {
        "embeddings": [
          {
            "vector": [
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            "num_dimensions": 1024
          }
        ]
      }
    }
  ]
}

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General embedding

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Textures and patterns

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