Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
ONNX
Safetensors
OpenVINO
distilbert
feature-extraction
text-embeddings-inference
Instructions to use sentence-transformers/distiluse-base-multilingual-cased-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sentence-transformers/distiluse-base-multilingual-cased-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sentence-transformers/distiluse-base-multilingual-cased-v2") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Update README.md
#11
by FischerWells - opened
I believe this is actually a 768 dimension dense model
Hello!
I believe the 512 is correct. The 2_Dense module is applied last, which maps the embeddings from 768 to 512 dimensions: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2/blob/main/2_Dense/config.json
- Tom Aarsen