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

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)