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bge-large-en-v1.5-onnx-o4
hooman650/bge-large-en-v1.5-onnx-o4
bge-large-en-v1.5-onnx-o4 is a feature extraction model from hooman650. Use it when you need embeddings to search or compare text. It is set up for transformers. The card lists the license as mit.
This is an ONNX O4 strategy optimized version of BAAI/bge-large-en-v1.5 optimal for Cuda. It should be much faster than the original version.
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At a glance
- Task
- Feature Extraction
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Nov 21, 2023
- Updated
- Nov 22, 2023
- SHA
- e7d321a3
Model card
From the Hugging Face model README
BGE-Large-En-V1.5-ONNX-O4
This is an ONNX O4 strategy optimized version of BAAI/bge-large-en-v1.5 optimal for Cuda. It should be much faster than the original
version.
Usage
# 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)
- Task
- Feature Extraction
- Library
- transformers
- Type
- bert
- License
- mit
- Languages
- en
- Created
- Nov 21, 2023
- Updated
- Nov 22, 2023