Sentence Similarity
sentence-transformers
Safetensors
Transformers
Russian
English
xlm-roberta
feature-extraction
text-embeddings-inference
Instructions to use BorisTM/bge-m3-unsupervised_en_ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BorisTM/bge-m3-unsupervised_en_ru with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BorisTM/bge-m3-unsupervised_en_ru") sentences = [ "Это счастливый человек", "Это счастливая собака", "Это очень счастливый человек", "Сегодня солнечный день" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use BorisTM/bge-m3-unsupervised_en_ru with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BorisTM/bge-m3-unsupervised_en_ru") model = AutoModel.from_pretrained("BorisTM/bge-m3-unsupervised_en_ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from BorisTM/bge-m3-unsupervised_en_ru: direct link, hf CLI and curl.
- Browser
- Download file 3.33 MB
-
https://huggingface.co/BorisTM/bge-m3-unsupervised_en_ru/resolve/main/tokenizer.json
- Command line
-
hf download hf://BorisTM/bge-m3-unsupervised_en_ru/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BorisTM/bge-m3-unsupervised_en_ru/resolve/main/tokenizer.json
3.33 MB
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