Overview

The universal and efficient token-level model can perform many token classification tasks. It achieves good generalization as GPT4 does for generation tasks, but does it precisely for token-level tasks such as Named Entity Recognition, Q&A, text cleaning, etc. You just need to formulate a prompt, and the model will recognize pieces of text that match your query.

What you need:

• put input prompt

What you get:

• list of text spans:
  • Information about recognized span: start, end, text

Key advantages:

• Good generalization to new examples;
• Interpratable probability scoring of outputs;
• High precision;
• Best on the market performance;

Use cases

Named Entity Recognition - recognition and classification of entities according to your custom classification system;
Question-Answering - finding an answer in a text to your custom question;
Text cleaning - cleaning text from ads and other irrelevant information;
Text summarization - highlighting in the text the most relevant and informative parts;
Coreference resolution - finding entities that belong to the same entity cluster;\

Support

1. Documentation: for in-depth information on our API, its functionalities, and integration guidelines, please refer to our documentation.
2. Discord: Join our Discord channel for rapid assistance and engage with our community and support team.
3. Email: Reach out to us at info@knowledgator.com.
4. Our website

Check our other APIs

• Comprehend-it - fast zero-shot text classification service to categorize texts at a scale;
• Text2Table - extract any table from the text just putting column names and text itself;
• Web2Meaning - comprehensive web scraper that extracts text from the selected web pages;
• Zero-shot NER - multi-domain NER system to recognize and classify any entities according to your custom classification;