potion-base-8M Model Card
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.
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/potion-base-8M")
# Compute text embeddings
embeddings = 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.
The results for this model can be found on the Model2Vec results page.
Additional Resources
- All Model2Vec models on the hub
- Model2Vec Repo
- Tokenlearn 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},
}
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Evaluation results
- accuracy on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported72.151
- ap on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported20.626
- ap_weighted on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported20.626
- f1 on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported59.187
- f1_weighted on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported77.349
- main_score on MTEB AmazonCounterfactualClassification (en-ext)test set self-reported72.151
- accuracy on MTEB AmazonCounterfactualClassification (en)test set self-reported71.791
- ap on MTEB AmazonCounterfactualClassification (en)test set self-reported33.038
- ap_weighted on MTEB AmazonCounterfactualClassification (en)test set self-reported33.038
- f1 on MTEB AmazonCounterfactualClassification (en)test set self-reported65.038