Sentence Similarity
sentence-transformers
PyTorch
English
bert
feature-extraction
text-embeddings-inference
Instructions to use flax-sentence-embeddings/all_datasets_v3_MiniLM-L6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use flax-sentence-embeddings/all_datasets_v3_MiniLM-L6 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("flax-sentence-embeddings/all_datasets_v3_MiniLM-L6") 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 flax-sentence-embeddings/all_datasets_v3_MiniLM-L6: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/flax-sentence-embeddings/all_datasets_v3_MiniLM-L6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://flax-sentence-embeddings/all_datasets_v3_MiniLM-L6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/flax-sentence-embeddings/all_datasets_v3_MiniLM-L6/resolve/main/pytorch_model.bin
90.9 MB
- Xet hash:
- 707e231160f3892cc5f1d042c7c7d306d57b09e6afb93f4ee442e8f3885ed24c
- Size of remote file:
- 90.9 MB
- SHA256:
- 1c32ef85f7d91b0c792be77e8ba461b216ed9500f641f51415733b6b90fe4d05
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