potion-base-4M 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-4M")

# 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.

Results

Model	Avg (All)	Avg (MTEB)	Class	Clust	PairClass	Rank	Ret	STS	Sum	Pearl	WordSim
all-MiniLM-L6-v2	55.80	55.93	69.25	44.90	82.37	47.14	42.92	78.95	25.96	60.83	49.91
potion-base-32M	52.83	52.13	71.70	41.25	78.17	42.45	32.67	73.93	24.74	55.37	55.15
potion-base-8M	51.32	51.08	70.34	39.74	76.62	41.79	31.11	72.91	25.06	53.54	50.75
potion-base-4M	50.01	49.77	68.00	39.47	75.37	41.41	28.43	71.87	23.82	52.55	49.21
M2V_base_output	48.77	47.96	66.84	33.96	74.90	39.31	25.36	68.76	26.61	54.02	49.18
potion-base-2M	47.55	47.49	64.13	37.53	73.72	40.46	22.99	69.77	23.80	50.82	44.72
GloVe_300d	45.49	45.82	62.73	37.10	72.48	38.28	21.80	61.52	26.81	45.65	43.05
BPEmb_50k_300d	42.33	41.74	61.72	35.17	57.86	37.26	15.36	55.30	29.49	47.56	41.28

The results show that potion-base-4M reaches 89.63% of the performance of all-MiniLM-L6-v2 with an average score of 50.01 while being orders of magnitude faster.

For full results, see the MTEB leaderboard.

Additional Resources

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,
  author       = {Stephan Tulkens and {van Dongen}, Thomas},
  title        = {Model2Vec: Fast State-of-the-Art Static Embeddings},
  year         = {2024},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.17270888},
  url          = {https://github.com/MinishLab/model2vec},
  license      = {MIT}
}

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potion-base-4M Model Card

from model2vec import StaticModel # Load a pretrained Model2Vec model model = StaticModel.from_pretrained("minishlab/potion-base-4M") # Compute text embeddings embeddings = model.encode(["Example sentence"])

How it works

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.