---
base_model: jinaai/jina-embeddings-v3
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
library_name: model2vec
license: mit
model_name: onnx
tags:
- embeddings
- static-embeddings
- sentence-transformers
---

# alikia2x/jina-embedding-v3-m2v-1024

This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the 
[jinaai/jina-embeddings-v3](https://huggingface.co/jinaai/jina-embeddings-v3) 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

### Via `model2vec`

Load this model using the `from_pretrained` method:

```python
from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("alikia2x/jina-embedding-v3-m2v-1024")

# Compute text embeddings
embeddings = model.encode(["Hello"])
```

### Via `sentence-transformers`

```bash
pip install sentence-transformers
```

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("alikia2x/jina-embedding-v3-m2v-1024")

# embedding:
# array([[ 1.1825741e-01, -1.2899181e-02, -1.0492010e-01, ...,
#          1.1131058e-03,  8.2779792e-04, -7.6874542e-08]],
#       shape=(1, 1024), dtype=float32)
embeddings = model.encode(["Hello"])
```

### Via ONNX

```bash
pip install onnxruntime transformers
```

You need to download `onnx/model.onnx` in this repository first.

```python
import onnxruntime
from transformers import AutoTokenizer
import numpy as np

tokenizer_model = "alikia2x/jina-embedding-v3-m2v-1024"
onnx_embedding_path = "path/to/your/model.onnx"

texts = ["Hello"]
tokenizer = AutoTokenizer.from_pretrained(tokenizer_model)
session = onnxruntime.InferenceSession(onnx_embedding_path)

inputs = tokenizer(texts, add_special_tokens=False, return_tensors="np")
input_ids = inputs["input_ids"]
lengths = [len(seq) for seq in input_ids[:-1]]
offsets = [0] + np.cumsum(lengths).tolist()
flattened_input_ids = input_ids.flatten().astype(np.int64)

inputs = {
    "input_ids": flattened_input_ids,
    "offsets": np.array(offsets, dtype=np.int64),
}

outputs = session.run(None, inputs)
embeddings = outputs[0]
embeddings = embeddings.flatten()

# [ 1.1825741e-01 -1.2899181e-02 -1.0492010e-01 ...  1.1131058e-03
#   8.2779792e-04 -7.6874542e-08]
print(embeddings)
```

Note: A quantized (INT8) version of this model is also available, offering reduced memory usage with minimal performance impact.
Simply replace `onnx/model.onnx` with the `onnx/model_INT8.onnx` file.
Our testing shows less than a 1% drop in the F1 score on a real down-stream task.

## 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)

## Library Authors

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},
}
```