The Text Veracity API is designed to discern whether textual content comes from an artificial intelligence (AI) model or a human writer. With the increasing prevalence of AI-generated content across platforms and industries, there is a growing need to authenticate textual content and maintain transparency in communication.
Leveraging advanced natural language processing (NLP) techniques and machine learning algorithms, the API examines and evaluates the linguistic attributes of text passages. By juxtaposing syntactic and semantic patterns present in the input text with known features of AI-generated content, the API deftly identifies cases where the text is likely to have been generated by AI.
Applicable to a variety of sectors and industries where textual content integrity is important, the API plays a vital role. In journalism and media, for example, it helps news organizations and editors mark up AI-generated articles or reports without human intervention or editorial scrutiny. This capability enables news outlets to respect editorial standards and preserve the integrity of their reporting.
In essence, the Text Veracity API provides a valuable resource for validating the authenticity of textual content and detecting instances of AI-generated text in various applications and industries. Leveraging advanced NLP methodologies and machine learning algorithms, the API facilitates transparency, integrity and reliability of textual communication.
It will receive a parameter and provide you with a text.
Besides the number of API calls, there is no other limitation.
To use this endpoint you must indicate text in the parameter.
Content Detection - Endpoint Features
Object | Description |
---|---|
text |
[Required] Indicates a text |
{"all_tokens": 28, "used_tokens": 28, "real_probability": 0.9850060343742371, "fake_probability": 0.014993980526924133}
curl --location --request GET 'https://zylalabs.com/api/3842/text+veracity+api/4488/content+detection?text="In the expansive digital landscape, algorithms tirelessly generate words, intricately weaving a tapestry of ideas and concepts that surpass human boundaries."' --header 'Authorization: Bearer YOUR_API_KEY'
Header | Description |
---|---|
Authorization
|
[Required] Should be Bearer access_key . See "Your API Access Key" above when you are subscribed. |
No long-term commitment. Upgrade, downgrade, or cancel anytime. Free Trial includes up to 50 requests.
The Text Veracity API determines whether a given text was written by a human or generated by an artificial intelligence model.
Users only need to enter a text.
There are different plans to suit all tastes, including a free trial for a small number of requests, but your rate is limited to avoid abuse of the service.
Zyla provides a wide range of integration methods for almost all programming languages. You can use these codes to integrate with your project as you need.
The Content Detection endpoint returns a JSON object containing probabilities indicating whether the text is likely human-written or AI-generated, along with token usage information.
The key fields in the response include "real_probability" (likelihood the text is human-written), "fake_probability" (likelihood the text is AI-generated), "all_tokens" (total tokens in the input), and "used_tokens" (tokens processed).
The response data is structured as a JSON object with key-value pairs, making it easy to parse and extract relevant information about the text's authenticity.
The primary parameter for the Content Detection endpoint is the text input, which users must provide to analyze its origin.
Users can utilize the returned probabilities to assess the authenticity of the text. For example, a high "real_probability" indicates human authorship, while a high "fake_probability" suggests AI generation.
Typical use cases include verifying news articles for authenticity, moderating user-generated content, ensuring academic integrity, and maintaining journalistic standards by detecting AI-generated text.
Data accuracy is maintained through advanced natural language processing techniques and machine learning algorithms that continuously learn from new text patterns and improve detection capabilities.
The API employs rigorous testing against a diverse dataset of known human and AI-generated texts to ensure reliable detection and minimize false positives or negatives.
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