Sentence Similarity
sentence-transformers
PyTorch
roberta
feature-extraction
text-embeddings-inference
Instructions to use flax-sentence-embeddings/st-codesearch-distilroberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use flax-sentence-embeddings/st-codesearch-distilroberta-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("flax-sentence-embeddings/st-codesearch-distilroberta-base") 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] - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from flax-sentence-embeddings/st-codesearch-distilroberta-base: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/flax-sentence-embeddings/st-codesearch-distilroberta-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://flax-sentence-embeddings/st-codesearch-distilroberta-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/flax-sentence-embeddings/st-codesearch-distilroberta-base/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- afa113ef4876297c572b7653536562d17816b0604ee8b23726448d8979b9645f
- Size of remote file:
- 329 MB
- SHA256:
- eec3c58b3fd1ca767f783848b856c58c38dcaaab8904d267cd55e11387b28b16
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