Sentence Similarity
sentence-transformers
PyTorch
Transformers
Russian
bert
feature-extraction
text-embeddings-inference
Instructions to use inkoziev/sbert_synonymy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use inkoziev/sbert_synonymy with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("inkoziev/sbert_synonymy") sentences = [ "Кошка ловит мышку", "Мышка преследуема кошкой", "Кошка гонится за мышью", "Кошка ловит кайф" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use inkoziev/sbert_synonymy with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("inkoziev/sbert_synonymy") model = AutoModel.from_pretrained("inkoziev/sbert_synonymy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:eda9f2cd5a39a2e4d5ef72a41991bae8010a0775ee59dbf345e079ad36b24183
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size 116797656
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