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", device_map="auto") - 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
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* Base model [FractalGPT/SbertDistil](https://huggingface.co/FractalGPT/SbertDistil).
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* Log of additional training after decomposition.
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<img src="https://github.com/FractalGPT/
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## Full Model Architecture
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```
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* Base model [FractalGPT/SbertDistil](https://huggingface.co/FractalGPT/SbertDistil).
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* Log of additional training after decomposition.
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<img src="https://github.com/FractalGPT/ModelEmbedderDistillation/blob/main/SbertSVDDistil/Train/en.JPG?raw=true" width=700 />
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## Full Model Architecture
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```
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