Text moderation is essential to providing safe and user friendly online communities and shopping experiences. The text moderation model takes text inputs and outputs classifications that indicate whether or not the given text passage is safe, or potentially harmful to your users and your brand.

Getting Started

First things first. You will need to set up a Clarifai account and create an application. This how-to article shows you how to label images with the Clarifai API and through Portal. If you would like to label images via API, you will also need to generate an API key.

Moderate text in Portal 

You can do almost anything that Clarifai can do with Clarifai Portal, and we work hard to make Portal the world's easiest interface for using AI. Classifying images with Portal is as simple as uploading your data, and choosing the right base workflow. 

Create your application and choose your base workflow

Simply log in to Clarifai Portal and create a new application. To use the wedding model, select "Text Moderation" as your base workflow. 

View your text passage in Explorer view

Upload an image to your application and view predictions in the righthand sidebar under the tab that says "App Workflow".

Moderate text in local file storage

Use the following Python snippet as an example of how to moderate a text file using the text moderation model. For more details and information on working with predictions in our other client languages, please refer to our API documentation.

from clarifai_grpc.grpc.api import service_pb2, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2

# This is how you authenticate.
metadata = (('authorization', 'Key {{YOUR_CLARIFAI_API_KEY}}'),)

post_model_outputs_response = stub.PostModelOutputs(

    service_pb2.PostModelOutputsRequest(

        model_id="{THE_MODEL_ID}",

        version_id="{THE_MODEL_VERSION_ID}",  # This is optional. Defaults to the latest model version.

        inputs=[

            resources_pb2.Input(

                data=resources_pb2.Data(

                    text=resources_pb2.Text(

                        base64=file_bytes

                    )

                )

            )

        ]

    ),

    metadata=metadata

)

if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:

    raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)




# Since we have one input, one output will exist here.

output = post_model_outputs_response.outputs[0]




print("Predicted concepts:")

for concept in output.data.concepts:

    print("%s %.2f" % (concept.name, concept.value))