Sentence Similarity
sentence-transformers
Safetensors
qwen3
lean4
mathlib
retrieval
premise-selection
text-embeddings-inference
Instructions to use hmzprover/hpsearch-embed-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hmzprover/hpsearch-embed-fast with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hmzprover/hpsearch-embed-fast") 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 tokenizer.json from hmzprover/hpsearch-embed-fast: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/hmzprover/hpsearch-embed-fast/resolve/main/tokenizer.json
- Command line
-
hf download hf://hmzprover/hpsearch-embed-fast/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/hmzprover/hpsearch-embed-fast/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 73e9be806c1e370a97d5b26e1855a439c8b584adf859b9a94f6a46f114c1d7d6
- Size of remote file:
- 11.4 MB
- SHA256:
- 8a500ea4da0203056e19c9900821da21b04ed29125a11a1fd792533165e40647
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