Instructions to use Qdrant/bge-large-en-v1.5-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qdrant/bge-large-en-v1.5-onnx with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Qdrant/bge-large-en-v1.5-onnx") model = AutoModel.from_pretrained("Qdrant/bge-large-en-v1.5-onnx") - Notebooks
- Google Colab
- Kaggle
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...])
# ]
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