# Patent Text Analysis

This is a Russian patent named entity recognition model based on the spaCy framework, focusing on identifying specific entity types in patent texts.

This is a model based on sentence-transformers that maps sentences and paragraphs into a 384-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

A patent contradiction sentence identification model based on PatentSBERTa, capable of mapping sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.

A Patent Problem and Solution Sentence Identification Model based on PatentSBERTa, which maps sentences and paragraphs into a 768-dimensional dense vector space, suitable for tasks such as clustering or semantic search.