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:

1. Distilling the BGE-M3 transformer (568M params) into a static token embedding lookup table using model2vec
2. 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)