Text Moderation Model

DeepLobe's pre-trained text moderation model is designed to analyze and classify text as acceptable or not based on community guidelines and predefined criteria. The model has been trained on a large dataset of text examples, where each example is labeled as acceptable or not, allowing it to learn patterns and features indicative of inappropriate or offensive content.

Working of the Text Moderation Model

During training, the model is exposed to a diverse set of text examples, along with corresponding labels indicating whether each example is considered acceptable or not. By adjusting its internal parameters, the model learns to accurately predict the acceptability of new text based on the patterns and features it has learned.

Testing Text Moderation

To test the capabilities of the text moderation model, follow these steps:

1. Enter the text in the designated text box.
2. Click the "Run model" button to initiate the text moderation process.
3. The model will analyze the input text and provide probability scores indicating the likelihood of questionable or unacceptable content.

API Integration

If you are satisfied with the text moderation results obtained, you can seamlessly integrate the model into your existing systems or applications. Here's how:

1. Click on the "Sample code" button to access a few lines of code.
2. Copy and paste the provided code into your application.
3. Replace the placeholder "REPLACE_API_KEY" with the API key generated for your account.

For information on generating an API key, refer to the "Create an API Key" section in the documentation.

API Documentation

For comprehensive information on using the Text Moderation model via the API, consult our API documentation. It provides detailed guidance and examples to help you effectively leverage the model within your applications.

We hope the Text Moderation model enhances your ability to maintain a safe and respectful environment for user-generated content across various online platforms.

If you have any questions or require further assistance regarding the Text Moderation model or any other aspect of DeepLobe, please reach out to our support team.