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m2v-bge-m3-european
flipbitsnotburgers/m2v-bge-m3-european
m2v-bge-m3-european is a machine learning model from flipbitsnotburgers. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for model2vec. The card lists the license as mit.
A Model2Vec static embedding model distilled from BAAI/bge-m3, pruned to European languages only.
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81.3 MB on disk
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At a glance
- Library
- model2vec
- License
- mit
- Model type
- model2vec
- Access
- Public
- Created
- Apr 16, 2026
- Updated
- Apr 16, 2026
- SHA
- bc25f81d
Base models
Model card
From the Hugging Face model README
m2v-bge-m3-european
A Model2Vec static embedding model distilled from BAAI/bge-m3, pruned to European languages only.
What is this?
This model was created by:
- Distilling the BGE-M3 transformer (568M params) into a static token embedding lookup table using model2vec
- Pruning all non-European script tokens (CJK, Arabic, Hebrew, Thai, Devanagari, Korean, Japanese, etc.)
The result is a lightweight embedding model that only contains tokens relevant to European languages (Latin, Greek, Cyrillic, etc.).
Stats
| Before pruning | After pruning | |
|---|---|---|
| Vocabulary | 249,999 tokens | 158,843 tokens |
| Model size | ~244 MB | ~78 MB |
| Embedding dim | 256 | 256 |
36.5% of tokens were removed (non-European scripts).
Usage
from model2vec import StaticModel
model = StaticModel.from_pretrained("flipbitsnotburgers/m2v-bge-m3-european")
embeddings = model.encode(["deodorant", "Duschgel", "shower gel"])
Pruned scripts
The following scripts were removed:
- CJK (Chinese, Japanese Kanji)
- Hangul (Korean)
- Hiragana & Katakana (Japanese)
- Arabic
- Hebrew
- Thai, Lao
- Devanagari, Bengali, Tamil, Telugu, and other Indic scripts
- Myanmar, Ethiopic, Tibetan, Khmer
License
MIT (same as base model)
- Library
- model2vec
- Type
- model2vec
- License
- mit
- Languages
- multilingual, de, en, fr, es, pl
- Created
- Apr 16, 2026
- Updated
- Apr 16, 2026