README.md

---
base_model: intfloat/e5-small-v2
language:
- en
library_name: model2vec
license: mit
model_name: e5-small-v2-distilled
tags:
- embeddings
- static-embeddings
pipeline_tag: sentence-similarity
---

<img src="https://takara.ai/images/logo-24/TakaraAi.svg" width="200" alt="Takara.ai Logo" />

From the Frontier Research Team at **Takara.ai** we present a distilled version of the e5-small-v2 model using Model2Vec technology for ultra-fast text embeddings.

---

# e5-small-v2-distilled

This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) 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("takara-ai/e5-small-v2-static-distilled")
# 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.

## Additional Resources

- [All Model2Vec models on the hub](https://huggingface.co/models?library=model2vec)
- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Model2Vec Results](https://github.com/MinishLab/model2vec?tab=readme-ov-file#results)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)

## Original Credits

Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).

## Citation

Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) 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},
}
```

---
For research inquiries and press, please reach out to [email protected]

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