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