NEW: Custom Text Classification available on Eden AI
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NEW: Custom Text Classification available on Eden AI

Quickly and easily classify text with just a few simple steps! Custom Text Classification allows users to classify and categorize their text by automatically setting themselves a label for them from a predefined set of categories.

What is Custom Text Classification API?

The practice of utilizing machine learning to classify text into particular categories is known as custom text classification. Custom models, as opposed to generic models, are designed for particular categories or domains.

It entails obtaining labeled text data, transforming text into numerical characteristics, choosing an effective classification algorithm, training the model, assessing its performance, deploying it for predictions, and iteratively improving it with new data and feedback. This method offers precise categorization in specialized domains including sentiment analysis, intent identification, and more.

custom text classification

Access many Custom Text Classification providers with one API

Our standardized API allows you to use different providers on Eden AI to easily integrate custom text classification APIs into your system and offer your users a convenient way to automatically classify and categorize the text.

Cohere - Available on Eden AI

Cohere provides a powerful custom text classification solution that makes use of cutting-edge machine learning methods. To satisfy domain-specific needs, this technique entails developing specialized models to classify text into separate groups. You may accelerate the data collection, preprocessing, feature extraction, model selection, training, assessment, deployment, and refinement processes by using Cohere's technology. A unique and precise text categorization system that excels in areas like sentiment analysis, intent recognition, and more is the end product.

OpenAI - Available on Eden AI

OpenAI's API empowers developers to harness their generative models for Custom Text Classification. Leveraging generative LLMs in classification taps into the context acquired during pre-training, resulting in enhanced performance even with minimal examples.

Benefits of using a Custom Text Classification API

Using a custom text classification API can provide several benefits for businesses and developers looking to classify text based on predefined categories by the user. Here are some advantages of using a Custom Text Classification API:

  1. Tailored to Specific Needs: A custom text classification API allows you to create a text classification model that is specifically designed for your domain or industry. This customization ensures that the model understands the nuances of your data and produces more accurate results.
  2. Improved Accuracy: Generic models may not fully capture the intricacies of your data. Custom models can achieve higher accuracy by considering context and domain-specific language patterns.
  3. Efficiency: Custom text classification APIs streamline the classification process, reducing the need for manual categorization. This saves time and resources, especially when dealing with large volumes of text.
  4. Scalability: APIs can handle varying levels of demand, ensuring your custom text classification needs are met as your user base grows.

What are the uses of Custom Text Classification APIs?

Custom text classification APIs have a wide range of uses across various industries and applications. Here are some everyday use cases: ‍

1. Spam Detection

Identification of unwanted or irrelevant messages, such as spam emails or comments, is the process of spam detection. By automatically filtering out such content, Custom Text Classification APIs assist email providers, social media platforms, and forums in maintaining a clean and user-friendly environment. These APIs are trained to recognize patterns that signal spam content.

2. Sentiment Analysis

Analyzing a text's emotional tone to identify whether it is favorable, negative, or neutral is known as sentiment analysis. To understand consumer perceptions about goods, services, or brands, market research professionals frequently utilize this application.

Businesses can track client sentiment in real-time with the use of custom text classification APIs, allowing them to respond to unfavorable comments and capitalize on positive ones.

3. Intent Recognition

Understanding user intent is essential for giving precise responses in apps like chatbots and virtual assistants. Custom Text Classification APIs can analyze user requests and classify them into distinct intents (such as making a reservation or requesting directions), allowing the system to produce pertinent and appropriate responses.

4. Content Tagging

Assigning labels or tags that are evocative to specific bits of content is known as content tagging. Custom Text Classification APIs make it simpler to organize and categorize text for effective retrieval and presentation on websites, apps, and databases by analyzing the content and determining the proper tags.

5. Product Classification

Effective product organization and presentation are essential in e-commerce. By classifying goods based on their characteristics, features, and descriptions, Custom Text Classification APIs can improve search functionality and enable tailored product suggestions.

6. Review Analysis

Utilizing Custom Text Classification APIs to analyze customer evaluations offers organizations insights into client feedback, assisting them in identifying common problems, trends, and advantages related to their goods and services. Strategic decisions and improvements are informed by this analysis.

How to use Custom Text Classification with the Eden AI API?

To start using Custom text classification you need to create an account on Eden AI for free. Then, you'll be able to get your API key directly from the homepage and use it with free credits offered by Eden AI.

Best Practices for Custom Text Classification on Eden AI

When implementing custom text classification on Eden AI or any other platform, it's essential to follow certain best practices to ensure optimal performance, accuracy, and security. Here are some general best practices for Custom text classification on Eden AI:

  1. Quality Data Collection: Gather a diverse and representative dataset that covers all possible categories you intend to classify. Ensuring high-quality and balanced data is crucial for training an effective model.
  2. Labeling Consistency: Ensure that labels assigned to your training data are consistent and accurate. Ambiguities in labeling can lead to confusion during model training and evaluation.
  3. Model Selection: Carefully choose the appropriate classification algorithm or architecture for your task. Consider factors like the complexity of the problem, the size of your dataset, and available computing resources.
  4. Ethical Considerations: Be aware of potential biases in your data and model. Take steps to mitigate bias and ensure fairness, particularly when dealing with sensitive topics or underrepresented groups.

How Eden AI can help you?

Eden AI is the future of AI usage in companies: our app allows you to call multiple AI APIs.

  • Centralized and fully monitored billing on Eden AI for all Custom text classification APIs
  • Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider
  • Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
  • The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines)
  • Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.

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