Welcome
Last updated
Last updated
Knowledgator specializes in developing open-source ML models designed for information extraction (IE) tasks. Our models are trained to work for a range of IE tasks:
Text classification
Named-entity recognition
Relation extraction
Entity linking
Question-answering
Reranking of search results
Text summarization
Text cleaning
Coreference resolution
Knowledgator currently provides two types of access to information extraction models:
Open-source access: our models are available under the on . These models can be freely downloaded, modified to suit your needs and run on your custom instances.
API as a Service: for users seeking ready-to-use solutions, we provide API services with flexible pricing plans to suit various scales and requirements, available on .
Comprehend-it
Next-generation sequence classification model designed for multi-label text classification tasks, with output scoring. Works in zero and few-shot learning settings. Could be used for:
Text classification
Reranking of search results
Named-entity recognition (NER)
Relation extraction
Entity linking
Q&A
Salamandra
Universal encoder-based token classification model capable of working with a wide range of popular IE tasks. The model is prompt-based and is seamlessly configurable through prompts. Supported IE tasks:
Named-entity recognition (NER)
Relation extraction
Summarization
Q&A
Text cleaning
Coreference resolution
LiqFit: Python framework used for few-shot fine-tuning of a classification model(Comprehend-it).
📣 Custom Solutions: If you have unique requirements or need tailored solutions, our team is here to assist. to explore custom development opportunities.
Join our community to engage in discussions about ML and information extraction, share your experiences, and connect with our engineering team. For enterprise inquiries, more information about our models, or requests for additional features, please reach out to our .