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") - Notebooks
- Google Colab
- Kaggle
Add metadata for dataset used to train model
#1
by davanstrien HF Staff - opened
This is a small PR to add the dataset used to train your model to the YAML metadata section. This metadata makes the link between the model and the dataset easier to see on the Hugging Face Hub.
Thank you for this PR!
inkoziev changed pull request status to merged