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Entity extraction


Recognize and classify any entity according to your custom classification.
We provide a high-performance, multi-domain named entities recognition (NER) system that not only returns a list of text chunks and their classes but also the exact location of an entity with a predicted label and its score. It gives you more control and is especially helpful for high-precision projects.
We provide several types of models and algorithms to perform zero-shot named entity recognition. Depending on requirements such as performance, recall, precision or interpretability, you can select the following endpoints.
Entity recognition in practice
Get a hands-on experience with our tools in the AI Playground, discovering text-mining capabilities with NLP models.

Entity extraction models


A high-performance but accurate probabilistic zero-shot named entity recognition model;
  • When to use: if it’s essential high-performance and you need to perform analysis of large massive of data;
  • Pros: high speed, scoring of outputs, returning location of an entity in a text;
  • Cons: not the best recall, can’t logically infer entity categories, can’t group entities;


A rule-based entity recognition and classification according to user-defined labels;
  • When to use: you need high recall and deterministic outputs;
  • Pros: high recall, scoring of outputs, return location of an entity in a text;
  • Cons: bad with cases, where it’s needed to recognize sub-parts of phrases;


Performs recognition and classification of entities according to the user description. It can logically infer an entity and its class. Moreover, you can group entities into a single cluster using this endpoint.
  • When to use: if it’s required, logically infer entities categories of group entities by some rule;
  • Pros: good accuracy, can logically infer entity categories, can group entities;
  • Cons: not the best speed, can’t provide locations of an entity, no scoring;

Key features

  1. 1.
    Multi-domain data inputs;
  2. 2.
    Zero-shot capabilities;
  3. 3.
    Scoring of outputs;
  4. 4.
    Defining the exact position of an entity in a text;
  5. 5.
    Grouping of entities by some rule;
  6. 6.
    Logical inference of entities categories;


Get Started

1. Get the API key

To start using NER you need to generate your API key and use it in your API requests. For guidance on this process, please refer to our authentication guide. If you already have one, ensure you're subscribed to NER API. To do so, head over to our page on Rapid API, select a model type and hit the "subscribe" button. Pick a plan that resonates with your requirements (we offer a free trial).

2. Make your first request

With your API key in hand, you are now allowed to make your first request to NER models. Kindly proceed to the API reference page to acquire knowledge on configuring an API request and to familiarize yourself with all the pertinent API details.


Is there a free trial option available for NER, and how can I access it?
Yes, a free trial is available. Please visit our pricing page for more details.
How can I contact the support team if I need help?
We provide immediate assistance for any cases, please feel free to reach out to our support team without hesitation. Check our support team contact page.
How can I ensure that I am being charged correctly for my API usage?
Keep track of your API usage in your account settings on the RapidAPI dashboard. Ensure you are on the appropriate pricing plan that suits your needs and monitor your request count to avoid unexpected charges.