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Knowledgator is an open-source ML research organization focused on expanding human knowledge through fundamental models for information extraction
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Our core is open-source. We believe in advancing AI through transparency and shared innovation, inviting you to join us in shaping technology for the common good
Focused on groundbreaking research, we develop ML solutions for information extraction that overcome the limitations of large-scale generative models, ensuring resource efficiency and task-specific precision
Access and tailor open ML solutions with unparalleled ease, ensuring our models integrate smoothly into your workflow
We offer custom fine-tuning and deployment services for your specific needs
Leveraging deep technical expertise for smart model optimization, delivering high performance with cost mindfulness
As creators and constant innovators of our technology, we offer unparalleled customization, tailoring our ML models to meet your unique needs
Consistent and comprehensive post-deployment assistance, ensuring our solutions evolve and remain effective alongside your usage
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Our baseline models achieve an 83% precision rate across diverse domains. Their compact size, 10 times smaller than alternatives generative models, opens vast potential for further scalability and performance optimization
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Whether it's a large document or a complex biological data, our models are equipped to process up to 100k tokens, ensuring no detail is missed
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Transparency and traceability are fundamental to our models. We employ symbolic logical reasoning to ensure the validation of outputs, delivering accurate and free from “hallucinations” results
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With fewer parameters, our models require less training data, simplifying the fine-tuning process. We're pioneering few-shot learning that needs only 10-20 examples per label for effective training
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Aiming to bridge modalities, we're enhancing our models to adeptly extract information from varied data types, including sequential data(DNA) and images, paving the way for truly universal information extraction capabilities
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Our models excel in speed, delivering efficient data processing, 6-8 abstracts / second, making them easier to deploy in a wide range of applications
We explore and demonstrate methods to enhance the reliability and adaptability of ML models for information extraction
Introducing open-source encoder-based models for Zero-Shot Universal Token Classification (UTC) may significantly advance Natural Language Processing (NLP), especially in open source.
Every developer experienced a training data deficit while fine-tuning the machine learning model. Large Language Models completely restructured the research landscape with zero-shot learning.
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In the realm of information extraction, text classification serves as a pivotal and ubiquitous task. Yet, the field of Natural Language Processing (NLP) grapples with a critical issue: the prevailing simplification...
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