Sentence Similarity
sentence-transformers
PyTorch
distilbert
feature-extraction
text-embeddings-inference
Instructions to use Kalaoke/embeddings_dense_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Kalaoke/embeddings_dense_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Kalaoke/embeddings_dense_model") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Kalaoke/embeddings_dense_model: direct link, hf CLI and curl.
- Browser
- Download file 265 MB
-
https://huggingface.co/Kalaoke/embeddings_dense_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Kalaoke/embeddings_dense_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Kalaoke/embeddings_dense_model/resolve/main/pytorch_model.bin
265 MB
- Xet hash:
- ab115b9b5dbf4b60361fbc1b309ce58aa0f46b9c5d62b0d4a967409ce582353c
- Size of remote file:
- 265 MB
- SHA256:
- 73adf2a9ac08929472147e58ac3de4d4fc3d1b95948d8724ce955c1accec7d24
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