Hybrid symbolic-deep learning language processing
We break down the pros and cons of both deep learning LLM (Large Language Models) and our innovative hybrid approach.
Our innovative hybrid approach
The Clover Programming Language allows to blend very efficient symbolic language processing with LLMs.
Simplicity, Access to large quantities of unprocessed data
Requires large, expensive training data and resources
Smaller models, Faster training with less data
None
Our services
At Clover.AI, we offer a range of services designed to enhance your language processing capabilities. Our expertise lies in high granularity named-entity detection, text-based routing based on categorization, and text flow processing. We provide fast and customizable solutions that cater to a diverse set of languages
Harness the power of precise named-entity recognition for your text data.
Optimize text routing and categorisation processes for improved efficiency.
Streamline the flow of text data for enhanced insights and decision-making.
Unlock the potential of language processing in over 40 languages.
Tailor our services to meet your specific language processing requirements. Advantages of Hybrid Symbolic-Deep Learning
Custom tokenizers, part-of-speech taggers, normalizers….
Extract from text logical statements that go beyond traditional fact extraction.
Check collaboration opportunities with Clover.AI
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