This Model2Vec model is a distilled version of the BAAI/bge-m3 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:
pip install model2vec
Usage
Load this model using the from_pretrained method:
python
1from model2vec import StaticModel
23# Load a pretrained Model2Vec model4model = StaticModel.from_pretrained("FlukeTJ/bge-m3-m2v-distilled-256")56# Compute text embeddings7embeddings = model.encode(["Example sentence"])
Alternatively, you can distill your own model using the distill method:
python
1from model2vec.distill import distill
23# Choose a Sentence Transformer model4model_name ="BAAI/bge-base-en-v1.5"56# Distill the model7m2v_model = distill(model_name=model_name, pca_dims=256)89# Save the model10m2v_model.save_pretrained("m2v_model")
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.
@software{minishlab2024model2vec,
authors = {Stephan Tulkens, Thomas van Dongen},
title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model},
year = {2024},
url = {https://github.com/MinishLab/model2vec},
}
