This Model2Vec model is pre-trained using Tokenlearn. It 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.
This model is pre-trained on the scientific_papers dataset consisting of scientific papers from Arxiv and Pubmed. It uses a larger vocabulary size than the potion-science-8M model which can be beneficial for tasks that require a larger (specialized) vocabulary.
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("minishlab/potion-science-32M")56# Compute text embeddings7embeddings = model.encode(["Example sentence"])
How it works
Model2vec creates a small, static model that outperforms other static embedding models by a large margin on all tasks on MTEB. This model is pre-trained using Tokenlearn. It's created using the following steps:
Distillation: first, a model is distilled from a sentence transformer model using Model2Vec.
Training data creation: the sentence transformer model is used to create training data by creating mean output embeddings on a large corpus.
Training: the distilled model is trained on the training data using Tokenlearn.
Post-training re-regularization: after training, the model is re-regularized by weighting the tokens based on their frequency, applying PCA, and finally applying SIF weighting.
@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},
}
