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Models for zero-shot text classification that are up to 50 times faster than Cross-Encoders and show the same or higher accuracy.

The Contradiction Psb Lds model is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space, allowing for tasks like clustering or semantic search. But what does that mean for you? It means you can use this model to identify contradiction sentences in patents with ease. It's built on top of the PatentSBERTa model and can be used with either sentence-transformers or HuggingFace Transformers. The model is efficient and can be used for a variety of tasks, but have you ever wondered how it was trained? It was trained with a batch size of 16 and a learning rate of 2e-05, with a total of 1128 steps per epoch. The model's architecture is based on the MPNetModel, which is a type of transformer model. So, what makes this model unique? It's ability to identify contradiction sentences in patents, making it a valuable tool for anyone working with patent data.

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