MinishLab / model2vec

What it does

• State-of-the-Art Performance. Model2Vec models outperform any other static embeddings (such as GLoVe and BPEmb) by a large margin, as can be seen in our results.
• Small. Model2Vec reduces the size of a Sentence Transformer model by a factor of up to 50. Our best model is just ~30 MB on disk, and our smallest model just ~8 MB (making it the smallest model on MTEB!).
• Lightweight Dependencies. the base package's only major dependency is numpy.
• Lightning-fast Inference. up to 500 times faster on CPU than the original model.
• Fast, Dataset-free Distillation. distill your own model in 30 seconds on a CPU, without a dataset.