---
license: mit
language:
- en
library_name: transformers
pipeline_tag: feature-extraction
---

# BGE-Large-En-V1.5-ONNX-O4

This is an `ONNX O4` strategy optimized version of [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) optimal for `Cuda`. It should be much faster than the original
version.

![](https://media.githubusercontent.com/media/huggingface/text-embeddings-inference/main/assets/bs32-tp.png)

## Usage

```python

# pip install "optimum[onnxruntime-gpu]" transformers

from optimum.onnxruntime import ORTModelForFeatureExtraction
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('hooman650/bge-large-en-v1.5-onnx-o4')
model = ORTModelForFeatureExtraction.from_pretrained('hooman650/bge-large-en-v1.5-onnx-o4')
model.to("cuda")

pairs = ["pandas usually live in the jungles"]
with torch.no_grad():
    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
    sentence_embeddings = model(**inputs)[0][:, 0]

# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
```