PaECTER a Patent Similarity Model PaECTER (Patent Embeddings using Citationinformed TransformERs) is a patent similarity model. Built upon Google's BERT for Patents as its base model, it generates 1024 dimensional dense vector embeddings from patent text. These vectors encapsulate the semantic essence of the given patent text, making it highly suitable for various downstream tasks related to patent analysis. Paper: https://arxiv.org/pdf/2402.19411 Applications Semantic Search Prior Art Search Clustering Patent Landscaping Usage (Sentence Transformers) Using this model becomes easy when you have sentence transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) Without sentence transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling operation on top of the contextualized word embeddings. Evaluation Results Evaluation of this model is available in our paper, PaECTER: Patent level Representation Learning using Citation informed Transformers Training The model was trained with the parameters: DataLoader : torch.utils.data.dataloader.DataLoader of length 318750…
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