---
pipeline_tag: sentence-similarity
language:
- ar
- en
license: mit
tags:
- passage-retrieval
- sentence-similarity
- pruned
library_name: sentence-transformers
base_model: BAAI/bge-m3
base_model_relation: quantized
---
# 🇸🇦 arabic-english-bge-m3

This model is a 36.2% smaller version of [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)
for the Arabic language.

RANKED #3 in general and #1 Open source @ [The-Arabic-Rag-Leaderboard](https://huggingface.co/spaces/Navid-AI/The-Arabic-Rag-Leaderboard)

The ONNX quantized version is around 75% smaller (363 MB) than the pruned model, while retaining approximately 98% of the original model's quality.

This pruned model should perform similarly to the original model for Arabic language tasks with a much smaller
memory footprint. However, it may not perform well for other languages present in the original multilingual model as tokens not
commonly used in Arabic were removed from the original multilingual model's vocabulary.


## Usage

You can use this model with the Transformers library:

```python
from transformers import AutoModel, AutoTokenizer

model_name = "sayed0am/arabic-english-bge-m3"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=True)
```

Or with the sentence-transformers library:

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("sayed0am/arabic-english-bge-m3")
```

Using it with OpenAI embedding endpoint using [infinity](https://github.com/michaelfeil/infinity)

```bash
#!/bin/bash

port=8000
model=sayed0am/arabic-english-bge-m3
volume=$PWD/data

docker run -it \
  -v $volume:/app/.cache \
  -p $port:$port \
  michaelf34/infinity:latest-cpu \
  v2 \
  --engine optimum \
  --model-id $model \
  --port $port \
  --url-prefix v1 \
  --api-key sk-123
```
Using ONNX
```python
# pip install huggingface-hub
 
from huggingface_hub import snapshot_download

snapshot_download(repo_id="sayed0am/arabic-english-bge-m3",local_dir="arabic-english-bge-m3")
```

```python
from optimum.onnxruntime import ORTModelForFeatureExtraction
from transformers import AutoTokenizer
import torch

# Make sure that you download the model weights locally to `bge-m3-onnx`
model = ORTModelForFeatureExtraction.from_pretrained("arabic-english-bge-m3", subfolder="onnx", provider="CUDAExecutionProvider") # omit provider for CPU usage.
tokenizer = AutoTokenizer.from_pretrained("arabic-english-bge-m3")
sentences = [
    "English: The quick brown fox jumps over the lazy dog.",
    "Arabic: الثعلب البني السريع يقفز فوق الكلب الكسول."
]
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt').to("cuda") # For CPU remove .to("cuda")

# Get the embeddings
out=model(**encoded_input,return_dict=True).last_hidden_state

# normalize the embeddings
dense_vecs = torch.nn.functional.normalize(out[:, 0], dim=-1)

```