Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Russian
bert
pretraining
russian
fill-mask
embeddings
masked-lm
tiny
feature-extraction
text-embeddings-inference
Instructions to use cointegrated/rubert-tiny2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cointegrated/rubert-tiny2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cointegrated/rubert-tiny2") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use cointegrated/rubert-tiny2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny2") model = AutoModelForPreTraining.from_pretrained("cointegrated/rubert-tiny2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5cea25291a6a473d0f33c1e8577709122f099b3c2a52c6987fe6e8ce1a3c9f6a
- Size of remote file:
- 118 MB
- SHA256:
- 137fa2b1d944dae19c74456dfe8fac2f780d9acf34e037f5d1e37acba1157768
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