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---
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.
## 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)
``` |