minishlab/M2V base output Model Card This Model2Vec model is a distilled version of the baai/bge base en v1.5 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real time performance is critical. Installation Install model2vec using pip: Usage Load this model using the from pretrained method: Alternatively, you can distill your own model using the distill method: How it works Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence. Results Model Avg (All) Avg (MTEB) Class Clust Pair…
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