Model reference · open weights
Splade_PP_en is an open-weight embedding model from Qdrant. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | Qdrant |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Context | 512 tokens |
| Runs with | transformers |
| Released | 2024-03-13 |
| Popularity | 253k downloads / month |
| Licence | Open weights |
About
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...]))
# ]
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys splade-pp-en for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (splade-pp-en below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"splade-pp-en","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.