Model reference · open weights

neodictabert-bilingual-embed

Available as managed deployment Embeddings dicta-il Embeddings 1 variants 3k dl/mo

neodictabert-bilingual-embed is an open-weight embedding model from dicta-il. 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 bydicta-il
TypeEmbedding models
TaskEmbeddings
Parameters (lead)363M
Runs withsentence-transformers
Based ondicta-il/neodictabert-bilingual
Released2026-02-02
Popularity3k downloads / month
LicenceOpen weights

About

What neodictabert-bilingual-embed is

This is a sentence-transformers model finetuned from dicta-il/neodictabert-bilingual on the he dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

This model achieved #10 on the private phase of the Hebrew Semantic Retrieval National Challenge.

Read the full model card

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("dicta-il/neodictabert-bilingual-embed", trust_remote_code=True)
# Run inference
queries = [
    "query: מתכון למיונז ביתי (חלמון, שמן, חרדל, לימון) + הוראות הכנה",
]

documents = [
    "מיונז ביתי. מרכיבים: חלמון בטמפרטורת החדר, חרדל דיז'ון, מיץ לימון/חומץ, מלח, שמן ניטרלי. הכנה: טורפים חלמון+חרדל+מלח+לימון, מזלפים שמן בהדרגה תוך טריפה עד להסמכה (אמולסיה).",
    "ים המלח. עובדות: זהו המקום הנמוך ביותר על פני היבשה, המליחות בו גבוהה בהרבה מהאוקיינוס ולכן אנשים צפים בקלות. בוץ עשיר במינרלים משמש גם לקוסמטיקה.",
    "כתב יתדות. היסטוריה: מסופוטמיה/שומר, חריתה בלוחות טיט בעזרת קנה. התפתח מאידיאוגרמות לייצוג פונטי והאפשר ניהול ביורוקרטי ושימור חוקים וידע.",
    "פסטה ברוטב עגבניות. מרכיבים: פסטה, עגבניות, שום, שמן זית, מלח. הכנה: מבשלים פסטה ומכינים רוטב עגבניות.",
]

query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.2235,  0.0164,  0.0822, -0.0282]])

Citation

If you use NeoDictaBERT in your research, please cite NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew

BibTeX:

@misc{shmidman2025neodictabertpushingfrontierbert,
      title={NeoDictaBERT: Pushing the Frontier of BERT models for Hebrew},
      author={Shaltiel Shmidman and Avi Shmidman and Moshe Koppel},
      year={2025},
      eprint={2510.20386},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2510.20386},
}

License

Shield: CC BY 4.0

This work is licensed under a Creative Commons Attribution 4.0 International License.

CC BY 4.0

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys neodictabert-bilingual-embed for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (neodictabert-bilingual-embed 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":"neodictabert-bilingual-embed","input":"text to embed"}'

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