base_model, language, library_name, license, model_name, tags

base_model	language	library_name	license	model_name	tags
sentence-transformers/LaBSE	multilingual af sq am ar hy as az eu be bn bs bg my ca ceb zh co hr cs da nl en eo et fi fr fy gl ka de el gu ht ha haw he hi hmn hu is ig id ga it ja jv kn kk km rw ko ku ky lo la lv lt lb mk mg ms ml mt mi mr mn ne no ny or fa pl pt pa ro ru sm gd sr st sn si sk sl so es su sw sv tl tg ta tt te th bo tr tk ug uk ur uz vi cy wo xh yi yo zu	model2vec	mit	minishlab/m2v_multilingual_output	embeddings static-embeddings sentence-transformers

minishlab/m2v_multilingual_output Model Card

This Model2Vec model is a distilled version of the sentence-transformers/LaBSE 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:

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("minishlab/m2v_multilingual_output")

# Compute text embeddings
embeddings = model.encode(["Example sentence"])

Alternatively, you can distill your own model using the distill method:

from model2vec.distill import distill

# Choose a Sentence Transformer model
model_name = "BAAI/bge-base-en-v1.5"

# Distill the model
m2v_model = distill(model_name=model_name, pca_dims=256)

# Save the model
m2v_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.

Additional Resources

• All Model2Vec models on the hub
• Model2Vec Repo
• Model2Vec Results
• Model2Vec Tutorials

Library Authors

Model2Vec was developed by the Minish Lab team consisting of Stephan Tulkens and Thomas van Dongen.

Citation

Please cite the Model2Vec repository if you use this model in your work.

@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},
}