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...])# ]
Runs of Qdrant bge-large-en-v1.5-onnx on huggingface.co
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More Information About bge-large-en-v1.5-onnx huggingface.co Model
bge-large-en-v1.5-onnx huggingface.co is an AI model on huggingface.co that provides bge-large-en-v1.5-onnx's model effect (), which can be used instantly with this Qdrant bge-large-en-v1.5-onnx model. huggingface.co supports a free trial of the bge-large-en-v1.5-onnx model, and also provides paid use of the bge-large-en-v1.5-onnx. Support call bge-large-en-v1.5-onnx model through api, including Node.js, Python, http.
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bge-large-en-v1.5-onnx is an open source model from GitHub that offers a free installation service, and any user can find bge-large-en-v1.5-onnx on GitHub to install. At the same time, huggingface.co provides the effect of bge-large-en-v1.5-onnx install, users can directly use bge-large-en-v1.5-onnx installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
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