Model reference · open weights
Splade_PP_en is an open-weight embedding model from Qdrant.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the Qdrant/Splade_PP_en_v1 model card, 2026-10-01
What it is
| Released by | Qdrant |
|---|---|
| Released | 2024-03-13 |
From the model card
ONNX port of prithivida/Splade_PP_en_v1 for text classification and similarity searches.
Here's an example of performing inference using the model with FastEmbed.
from fastembed import SparseTextEmbedding
documents = [
"You should stay, study and sprint.",
"History can only prepare us to be surprised yet again.",
]
model = SparseTextEmbedding(model_name="prithivida/Splade_PP_en_v1")
embeddings = list(model.embed(documents))
# [
# SparseEmbedding(values=array(
# [0.45940185, 0.64054322, 0.2425732, 0.1623179, 1.20566428,
# 0.62039357...]),
# indices=array([1012, 1998, 2000, 2005, 2017, 2022...])),
# SparseEmbedding(values=array([
# 0.09767706, 0.4374367, 0.00468039, 1.01167965, 1.02318227, 1.30155718
# ...]),
# indices=array([2017, 2022, 2025, 2057, 2064, 2069...]))
# ]
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.