Feature Extraction
sentence-transformers
Russian
xlm-roberta
quantization
int8
text-embeddings-inference
Instructions to use AtesiT/bge-m3-int8-dynamic-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AtesiT/bge-m3-int8-dynamic-quantized with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AtesiT/bge-m3-int8-dynamic-quantized") 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
- Xet hash:
- 58dccb6182282912d112fe6592eeb224e235ffcb5a9acea32ce16eabd1285e3c
- Size of remote file:
- 1.36 GB
- SHA256:
- 225ff4eddb841e31148e55b4f629b1bdfeb771f34890958d702da903d3768c4c
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