DeepPavlov Russian fastText Static Embeddings

This repository provides a Sentence-Transformers StaticEmbedding conversion of DeepPavlov's ft_native_300_ru_wiki_lenta_lower_case Russian fastText vectors.

The source vectors are unchanged. The model adds an explicit Normalize module after StaticEmbedding, so model.encode(...) returns L2-normalized sentence embeddings by default.

Source

DeepPavlov publishes Russian word vectors trained on Russian Wikipedia and Lenta.ru corpora.

• Source docs: https://docs.deeppavlov.ai/en/0.0.6.5/intro/pretrained_vectors.html
• Source .vec: https://files.deeppavlov.ai/embeddings/ft_native_300_ru_wiki_lenta_lower_case/ft_native_300_ru_wiki_lenta_lower_case.vec
• Source model: ft_native_300_ru_wiki_lenta_lower_case
• Source preprocessing: nltk word_tokenize + lowercasing
• Training family: fastText skip-gram, 300 dimensions
• License: Apache 2.0

This conversion uses the text .vec vocabulary. It does not include fastText .bin subword OOV generation; out-of-vocabulary tokens map to [UNK].

Usage

pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer("BorisTM/deeppavlov_ft_native_300_ru_wiki_lenta_lower_case")

sentences = [
    "Сегодня хорошая погода.",
    "На улице солнечно.",
    "Команда выиграла матч.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)  # (3, 300)
print(np.linalg.norm(embeddings, axis=1))  # close to 1.0

similarities = model.similarity(embeddings, embeddings)
print(similarities)

Results

Results on MTEB (rus, v1.1), evaluated with normalized embeddings. Scores are percentages.

Evaluation artifacts for this update are stored locally in the article workspace under data/metrics/deeppavlov_fasttext_mteb_rus_v1_1_summary.csv and data/metrics/deeppavlov_fasttext_mteb_rus_v1_1_task_scores.csv.