This Model2Vec model 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:

python

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("minishlab/M2V_base_output")

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

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

python

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.

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 M2V_base_output reaches 87.40% of the performance of all-MiniLM-L6-v2 with an average score of 48.77 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.

bibtex

@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}
}