Sentence Similarity
sentence-transformers
Safetensors
Transformers
Russian
bert
feature-extraction
text-embeddings-inference
Instructions to use snagbreac/russian-reverse-dictionary-semsearch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use snagbreac/russian-reverse-dictionary-semsearch with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("snagbreac/russian-reverse-dictionary-semsearch") 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 snagbreac/russian-reverse-dictionary-semsearch with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("snagbreac/russian-reverse-dictionary-semsearch") model = AutoModel.from_pretrained("snagbreac/russian-reverse-dictionary-semsearch") - Notebooks
- Google Colab
- Kaggle
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