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