Sentence Similarity
sentence-transformers
PyTorch
Transformers
Russian
bert
feature-extraction
text-embeddings-inference
Instructions to use inkoziev/sbert_pq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use inkoziev/sbert_pq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("inkoziev/sbert_pq") sentences = [ "Кошка ловит мышку.", "Кто ловит мышку?", "Где живет кошка?", "Как мышку зовут?" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use inkoziev/sbert_pq with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("inkoziev/sbert_pq") model = AutoModel.from_pretrained("inkoziev/sbert_pq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
model update after dataset extension
koziev ilya95549a6
-
model update for chatbot minor release
koziev ilya39365f2
-
mentioning cossim_f1 metrics for rubert-tiny and rubert-base
koziev ilyaaf5f111
-
model update after adding negative samples to the training dataset
koziev ilya8fd5e39
-
adding link to cointegrated/rubert-tiny2
koziev ilyaead875c -
minor changes if text and typos fixing
koziev ilyad4be35f -
making readme more human-friendly
koziev ilya7468ff7 -
minor changes in model names thru out the text
koziev ilya6aab6f6 -
modifying the widget
koziev ilyaee4f669 -
first release of pq-relevancy model
koziev ilya1c8d4f5 -
initial commit
69ce236