Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:33487
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use polinahitrun/labse-evn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use polinahitrun/labse-evn with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("polinahitrun/labse-evn") sentences = [ "\"Ника ши?\"", "\"Кто ты?\"", "Интересно.", "За диким оленем шли, а он без шкуры, так он их и оставлял позади." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc706d753e9f1b0960c5766ad584e4ed96ae1720a88a2fde9b2974237e4b28b8
- Size of remote file:
- 13.6 MB
- SHA256:
- 92262b29204f8fdc169a63f9005a0e311a16262cef4d96ecfe2a7ed638662ed3
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