base_model: Snowflake/snowflake-arctic-embed-l-v2.0
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
library_name: model2vec
license: mit
model_name: FlukeTJ/snowflake-arctic-embed-l-v2.0-m2v-distilled-256
tags:
- embeddings
- static-embeddings
FlukeTJ/snowflake-arctic-embed-l-v2.0-m2v-distilled-256 Model Card
This Model2Vec model is a distilled version of the Snowflake/snowflake-arctic-embed-l-v2.0 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("FlukeTJ/snowflake-arctic-embed-l-v2.0-m2v-distilled-256")
# 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
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},
}