The API employs advanced natural language processing algorithms and language models, analyzing linguistic features to accurately identify the language of the provided text.

The API supports over 50 languages, including major languages such as English, Spanish, Chinese, and many others. Refer to the API documentation for the complete list of supported languages.

The API is optimized for high accuracy in language detection. However, the precision may vary depending on the complexity of the text. Developers can fine-tune parameters for specific use cases.

The Text Language API is capable of recognizing language variants and common dialects. It is trained on diverse linguistic data to accommodate variations within supported languages.

Yes, the API may have limitations on the size of the text that can be processed in a single request. Developers should refer to the API documentation for details on text size constraints.

The Get Language endpoint returns the recognized language name and its corresponding 2-letter ISO language code for the provided text string.

The key fields in the response data are "language," which indicates the name of the detected language, and "iso," which provides the 2-letter ISO code for that language.

The returned data is structured in JSON format, containing two fields: "language" (string) and "iso" (string). For example: `{"iso": "en", "language": "English"}`.

The Get Language endpoint provides information about the detected language of the input text, including its name and ISO code, facilitating language identification for various applications.

The response data is organized as a JSON object with two main fields: "language" for the name of the detected language and "iso" for the corresponding ISO code, making it easy to parse and utilize.

Typical use cases include content moderation, customer support automation, and localization efforts, where identifying the language of text is crucial for tailored responses and actions.

Users can customize their requests by providing different text strings to the endpoint, allowing for the detection of various languages based on the input content.

Data accuracy is maintained through advanced natural language processing algorithms and continuous training on diverse linguistic datasets, ensuring reliable language identification across multiple languages.

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