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Qdrant/
multilingual-e5-large-onnx

Name: multilingual-e5-large-onnx
Availability: InStock

text-embeddings-inference

endpoints_compatible

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ONNX port of intfloat/multilingual-e5-large 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="intfloat/multilingual-e5-large")
embeddings = list(model.embed(documents))

# [
#     array([
#         0.00611658, 0.00068912, -0.0203846, ..., -0.01751488, -0.01174267,
#         0.01463472
#     ],
#           dtype=float32),
#     array([
#         0.00173448, -0.00329958, 0.01557874, ..., -0.01473586, 0.0281806,
#         -0.00448205
#     ],
#           dtype=float32)
# ]

Qdrant/multilingual-e5-large-onnx

Usage

Qdrant/
multilingual-e5-large-onnx