.png%3Falt%3Dmedia%26token%3D0665fdd4-be7d-4936-830b-623cd002856c)
.png%3Falt%3Dmedia%26token%3D0665fdd4-be7d-4936-830b-623cd002856c)
.png?alt=media&token=5aa75d81-d808-4f74-8aa1-57e017d36bb1)
Comment on page
👋
Welcome!
We at Knowledgator specialize in developing efficient NLP solutions for text-mining processes. The Knowledgator API can be applied to a wide range of text-mining tasks:
We offer a range of NLP models with different capabilities and efficient price points, as well as the ability to fine-tune models for your specific needs.
Get a hands-on experience with our tools in the AI Playground, discovering text-mining capabilities with NLP models.
Scrape information from websites, collecting a content database from articles, news, company websites, or any public resources.
Understand text meaning and efficiently filter or categorize information with a comprehensive text classification model.
- Text classification The text classification model dives into the meaning of the text, accurately assigning it to predefined labels. It smartly discerns various topics and details within the text, establishing its reliability.
Identify and extract only the important pieces of information from the texts.
- Named-entity recognition(NER), zero-shot learning The NER model goes through the words and finds important entities within the text(companies, products or anything else), and extracts them separately from the text. This way, it turns text into neat and useful bits of information. Moreover, NER was built with a zero-shot learning approach, that makes it adaptable to new information easily, without needing lots of extra teaching.
- Relation extraction(RE), zero-shot learning Find and pull out how things in the text are related, helping you see and extract just the important connections and facts. It’s made with a zero-shot learning method, meaning it gets new information quickly without needing a ton of extra training.
- Question & answering(QA) The Q&A model answers questions by finding the specific information in texts. For this task, we developed an experimental token-search model that does more than just answer the question; it highlights the exact words in the text that make up the answer, showing users straight away how it came up with the response.
- Text2Table Think of Text2Table as a super-organizer for your data. It takes raw text from anywhere(web, files) and turns it into structured tables tailored to your needs, making your data cleaner and more useful for whatever you need. Set the table structure, give it the texts, and it takes care of the rest.
Last modified 1mo ago