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bge-large-en-v1.5-onnx
Qdrant/bge-large-en-v1.5-onnx
bge-large-en-v1.5-onnx is a sentence similarity model from Qdrant. Use it when you need a score for how close two texts are. It is set up for transformers. The card lists the license as mit.
ONNX port of BAAI/bge-large-en-v1.5 for text classification and similarity searches.
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At a glance
- Task
- Sentence Similarity
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Jan 16, 2024
- Updated
- Sep 24, 2026
- SHA
- e93b9305
Model card
From the Hugging Face model README
ONNX port of BAAI/bge-large-en-v1.5 for text classification and similarity searches.
Usage
Here's an example of performing inference using the model with FastEmbed.
from fastembed import TextEmbedding
documents = [
"You should stay, study and sprint.",
"History can only prepare us to be surprised yet again.",
]
model = TextEmbedding(model_name="BAAI/bge-large-en-v1.5")
embeddings = list(model.embed(documents))
# [
# array([1.96449570e-02, 1.60677675e-02, 4.10149433e-02...]),
# array([-1.56669170e-02, -1.66313536e-02, -6.84525725e-03...])
# ]
- Task
- Sentence Similarity
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Jan 16, 2024
- Updated
- Sep 24, 2026