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multilingual-e5-large-onnx
Qdrant/multilingual-e5-large-onnx
multilingual-e5-large-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 intfloat/multilingual-e5-large for text classification and similarity searches.
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At a glance
- Task
- Sentence Similarity
- Library
- transformers
- License
- mit
- Model type
- xlm-roberta
- Access
- Public
- Created
- Jan 16, 2024
- Updated
- Sep 24, 2026
- SHA
- ac6781cd
Model card
From the Hugging Face model README
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)
# ]
- Task
- Sentence Similarity
- Library
- transformers
- Type
- xlm-roberta
- License
- mit
- Created
- Jan 16, 2024
- Updated
- Sep 24, 2026