Feature Extraction
Transformers
Safetensors
sentence-transformers
Russian
English
bert
sentence-similarity
text-embeddings-inference
Instructions to use FractalGPT/SbertSVDDistil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FractalGPT/SbertSVDDistil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FractalGPT/SbertSVDDistil")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FractalGPT/SbertSVDDistil") model = AutoModel.from_pretrained("FractalGPT/SbertSVDDistil") - sentence-transformers
How to use FractalGPT/SbertSVDDistil with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FractalGPT/SbertSVDDistil") 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] - Notebooks
- Google Colab
- Kaggle