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zeromodels / multi-qa-mpnet-base-dot-v1
Model's Last Updated: October 16 2026
sentence-similarity
Introduction of multi-qa-mpnet-base-dot-v1
Model Details of multi-qa-mpnet-base-dot-v1
Run sentence embeddings with Keras 3
zeromodels/multi-qa-mpnet-base-dot-v1
Pure- Keras 3 conversion of sentence-transformers/multi-qa-mpnet-base-dot-v1 for ZeroModels . It encodes text into 768-dimensional vectors using a mpnet backbone. Its pooling is cls , its embeddings are not normalized , and the intended scoring function is dot product . The conversion preserves the source model's embedding recipe; it does not introduce a new training run.
For details about training and intended use, see the original model card .
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # set before importing Keras / ZeroModels
from keras import ops
from zeromodels.models.sentence_transformer import (
SentenceTransformerModel,
SentenceTransformerTokenizer,
)
repo = "zeromodels/multi-qa-mpnet-base-dot-v1"
model = SentenceTransformerModel.from_weights(repo)
tokenizer = SentenceTransformerTokenizer.from_weights(repo)
texts = ["A person is reading a book.", "Someone reads a novel."]
embeddings = model.encode(texts, tokenizer=tokenizer)
scores = ops.convert_to_numpy(model.similarity(embeddings, embeddings))
print(embeddings.shape) # (2, 768)
print(scores)
model.similarity()
uses
dot product
, matching the source model. If you need
unit-length vectors for a checkpoint that is not normalized, pass
normalize=True
to
model.encode(...)
; this changes the original embedding
recipe and may change retrieval scores.
Model details
| Property | Value |
|---|---|
| Backbone |
mpnet
|
| Pooling |
cls
|
| Dense projection | none |
| Embedding dimension | 768 |
| L2 normalization | no |
| Scoring | dot product |
| Maximum input length | 512 tokens |
| Original checkpoint | sentence-transformers/multi-qa-mpnet-base-dot-v1 |
| Original revision |
17997f24dca0df1a4fed68894fb0e1e133e60482
|
All 13 converted variants use
from_weights("zeromodels/<variant>")
:
| Variant | Encoder and recipe |
|---|---|
all-mpnet-base-v2
|
MPNet · mean + normalize |
multi-qa-mpnet-base-dot-v1
|
MPNet · CLS + dot product |
all-distilroberta-v1
|
RoBERTa · mean + normalize |
all-MiniLM-L12-v2
|
BERT · mean + normalize |
multi-qa-distilbert-cos-v1
|
DistilBERT · mean + normalize |
all-MiniLM-L6-v2
|
BERT · mean + normalize |
multi-qa-MiniLM-L6-cos-v1
|
BERT · mean + normalize |
paraphrase-multilingual-mpnet-base-v2
|
XLM-R · mean |
paraphrase-albert-small-v2
|
ALBERT · mean |
paraphrase-multilingual-MiniLM-L12-v2
|
BERT · mean |
paraphrase-MiniLM-L3-v2
|
BERT · mean |
distiluse-base-multilingual-cased-v1
|
DistilBERT · mean + Dense |
distiluse-base-multilingual-cased-v2
|
DistilBERT · mean + Dense |
Tips
-
Set
KERAS_BACKENDbefore importing Keras or ZeroModels. Embedding parity was checked on the Torch backend; full TensorFlow/JAX parity is not claimed. - Use the tokenizer from the same repo as the model. It preserves the source truncation length of 512 tokens for this checkpoint.
-
For exact source loading without these converted Keras weights, use
SentenceTransformerModel.from_weights("hf:sentence-transformers/multi-qa-mpnet-base-dot-v1"). -
The repository contains
model.weights.h5,zm_config.json, andtokenizer.json. The pinned source commit is listed above.
Verification and attribution
The conversion was compared with Sentence Transformers at the pinned source
revision. Maximum embedding absolute difference:
5.06639e-07
; minimum embedding cosine:
1.00000000
; maximum similarity-score absolute
difference:
3.8147e-06
.
Special thanks to the Sentence Transformers team for the original checkpoint. The upstream model card does not declare a license. Check redistribution rights before using or redistributing these weights.
Runs of zeromodels multi-qa-mpnet-base-dot-v1 on huggingface.co
22
Total runs
8
24-hour runs
16
3-day runs
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30-day runs
More Information About multi-qa-mpnet-base-dot-v1 huggingface.co Model
Url of multi-qa-mpnet-base-dot-v1
multi-qa-mpnet-base-dot-v1 huggingface.co Url
Provider of multi-qa-mpnet-base-dot-v1 huggingface.co
Other API from zeromodels
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