Master the art of creating custom text classifiers

Master the art of creating custom text classifiers

Table of Contents:

1. Introduction
2. Creating a Custom Classifier with Pre-Made Templates 2.1. Trying a Pre-Made Classifier 2.2. Creating a Classifier from a Template
3. Offensive Speech Detector Template 3.1. Description and Use 3.2. Adding and Editing Labels
4. Creating a Classifier from Scratch 4.1. Adding Labels to the Classifier
5. Testing the Custom Classifiers
6. Invoking the Classifier in the REST API
7. Conclusion
8. Next Steps: Building a Real-Life Demo with Zapier 8.1. Automating Processes with Zapier 8.2. Implementing the Classifier in PHP or Python

Creating Custom Classifiers with Think.AI

In today's tutorial, we will explore how to Create custom classifiers using Think.AI's platform. Custom classifiers allow You to categorize and label text content Based on your specific requirements. We will cover the process of creating classifiers from pre-made templates as well as from scratch. These classifiers can be used for a variety of purposes, such as determining the department a message is addressed to or detecting offensive speech in online communities. So, let's dive in and learn how to make the most of Think.AI's classifier capabilities.

1. Introduction

Before we jump into the details, let's understand the concept of custom classifiers and their significance in text classification. Custom classifiers are machine learning models that can be trained to categorize text-based on specific criteria. These criteria can be predefined labels or custom ones created according to your needs. Think.AI provides a user-friendly platform to create and train these custom classifiers, allowing you to classify text at Scale and automate processes.

2. Creating a Custom Classifier with Pre-Made Templates

2.1. Trying a Pre-Made Classifier

To get started, let's explore the option of using pre-made classifier templates. Think.AI offers a range of templates that you can choose from based on your requirements. For this tutorial, we will try out the "Customer Support Classifier." This classifier is designed to determine which department in an organization a message is addressed to, such as support, sales, marketing, or technical support.

To test this classifier, simply input a text message and observe the label assigned to it. For example, if you write "I need help with my Website," the classifier will label it as "Technical Support." The classification is based on pre-defined logic set up within your organization or workflow. This demonstrates how Think.AI's pre-made templates can quickly classify text based on the provided labels.

2.2. Creating a Classifier from a Template

If you want to create a customized classifier based on a template, here's what you need to do. On the classifier page, click on the "New" button. You will be presented with two options: "Create New Classifier From Scratch" and "Create From Template." Click on "Create From Template" to proceed.

Currently, there are a limited number of pre-existing templates available. However, Think.AI is constantly adding new templates to cater to a wider range of needs. Choose the template that best matches your requirements. For this tutorial, we will select the "Offensive Speech Detector" template. This template functions as a profanity detector and helps identify content that is considered offensive.

After selecting the template, provide a name for your classifier and a brief description. These details are for your own reference and do not impact the classification process. Once you click on "Create," the classifier will be created with the provided template.

3. Offensive Speech Detector Template

3.1. Description and Use

The Offensive Speech Detector template is specifically designed to identify offensive speech or profanity in online communities. It helps moderators filter out offensive content more efficiently. The template comes with pre-defined labels for offensive and non-offensive content, allowing you to classify new text based on these labels.

To use the Offensive Speech Detector, simply input a text and observe the assigned label. The classifier will provide a confidence level indicating how likely the content is offensive. For example, if you write "I like you," the classifier will label it as "Not Offensive" with a confidence level of 94%. Conversely, if you write "You are stupid," the classifier will label it as "Offensive Speech" with a confidence level of 96%.

3.2. Adding and Editing Labels

You also have the option to add or edit labels within the Offensive Speech Detector template. If you want to customize the labels, simply navigate to the labels section and make the desired changes. Remember that you can add up to 10 labels per classifier, and it is generally recommended to keep the number of labels minimal for better classification accuracy.

For example, let's say you want to replace the "Not Offensive" label with something more fitting, like "Clean." Simply remove the existing label and add the new one. After saving the changes, your classifier will now use the "Clean" label instead of "Not Offensive" for classifying content.

4. Creating a Classifier from Scratch

If you prefer complete customization and want to build a classifier from scratch, Think.AI provides that option as well. On the classifier page, click on "New" and then select "Create New Classifier From Scratch."

Similar to the process described earlier, provide a name for your classifier and a brief description. For demonstration purposes, let's create a sports classifier. This classifier will help determine the Type of sport Mentioned in a piece of text. Add labels such as soccer, basketball, and baseball, representing the various sports you want the classifier to identify.

After saving the labels, you can return to the homepage. Now, you can test your classifier by inputting Relevant text and observing the assigned label. For example, if you write "I kicked the ball yesterday," the classifier will label it as "Soccer." Similarly, writing "I threw the ball at my teammates" will label it as "Basketball." This demonstrates how you can create a custom classifier from scratch and train it to classify text based on your unique needs.

5. Testing the Custom Classifiers

Once you have created your custom classifiers, it is essential to test their effectiveness and accuracy. Input various Texts that fall within the scope of your classifier and observe the assigned labels. This will help ensure that the classifier is accurately categorizing the text as per your requirements. Make any necessary adjustments to the labels or logic if needed.

Remember, the success of a custom classifier depends on rigorous testing and continuous improvement. Regularly monitor its performance and refine the classifiers to achieve optimal results.

6. Invoking the Classifier in the REST API

Apart from using the classifier directly on the Think.AI platform, you can also invoke it through the REST API. By accessing your API credentials, you can integrate the classifier into your own applications or processes.

To utilize the API, click on the provided link to access your account profile. From there, you can view the documentation for the classifier API, including details on how to pass the classifier ID as a parameter. This allows you to leverage the classifier's capabilities within your own coding projects.

7. Conclusion

In conclusion, creating custom classifiers with Think.AI's platform provides you with a powerful tool for automating text classification tasks. Whether you choose to use pre-made templates or build classifiers from scratch, Think.AI offers flexibility and accuracy in labeling and categorizing textual content. By utilizing custom classifiers, you can streamline processes, improve efficiency, and gain valuable insights from large volumes of text data.

8. Next Steps: Building a Real-Life Demo with Zapier

Now that you have grasped the basics of building custom classifiers, it's time to take it a step further. In the next video, we will walk you through building a real-life demo using Think.AI's classifiers and Zapier. Zapier enables you to automate various processes and integrate different applications seamlessly. We will explore how you can leverage Zapier to enhance the capabilities of the classifiers and make them an integral part of your workflow.

8.1. Automating Processes with Zapier

Zapier is a popular automation tool that connects multiple applications and allows you to create custom workflows. By integrating Think.AI's classifiers with Zapier, you can automate processes based on the classification results. For example, you can automatically send classified content to specific teams or trigger certain actions based on the classification outcome.

8.2. Implementing the Classifier in PHP or Python

For those who prefer coding, Think.AI provides API documentation for implementing the classifiers in various programming languages. You can refer to the documentation for PHP or Python to build customized solutions using the classifiers. This gives you the flexibility to integrate the classifiers into your existing codebase or develop entirely new applications based on your unique requirements.

Thank you for watching this tutorial on Think.AI's classifier capabilities. We hope you have gained valuable insights into creating and utilizing custom classifiers. If you have any questions, please feel free to reach out to us at Think.AI. Happy classifying!

Highlights:

• Think.AI's platform offers the ability to create custom classifiers for text classification.
• Custom classifiers can be created using pre-made templates or from scratch.
• Templates like the Offensive Speech Detector can help identify offensive content in online communities.
• Custom classifiers offer flexibility in labeling and categorizing text based on specific criteria.
• Think.AI allows users to test and refine their custom classifiers for accuracy and effectiveness.
• The classifiers can be invoked through the REST API, allowing integration into other applications or processes.
• Zapier can be used to automate processes based on the classification results, enhancing the functionality of the classifiers.
• API documentation is available for implementing the classifiers in programming languages like PHP and Python.

FAQ Q&A:

Q: How many labels can I add to a custom classifier? A: You can add up to 10 labels per classifier. However, it is recommended to keep the number of labels minimal for better classification accuracy.

Q: Can I edit or remove labels from a classifier? A: Yes, you can easily add, edit, or remove labels from a classifier at any time. Simply navigate to the labels section and make the desired changes.

Q: Can I integrate the custom classifiers into my own applications? A: Yes, Think.AI provides API documentation that allows you to invoke the classifiers through the REST API. You can integrate them into your own applications or processes using the provided documentation.

Q: How accurate are the custom classifiers? A: The accuracy of the custom classifiers depends on several factors, including the quality and diversity of the training data. Regular testing and refinement of the classifiers can help improve their accuracy over time.

Q: Can I use the classifiers with programming languages other than PHP and Python? A: Yes, Think.AI's API documentation provides guidelines and examples for implementing the classifiers in various programming languages. You can refer to the documentation for your preferred language to integrate the classifiers accordingly.