Sentence Similarity
sentence-transformers
Safetensors
modernbert
premise-selection
lean
mathlib
text-embeddings-inference
Instructions to use DAPS-Math/DAPS-text-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use DAPS-Math/DAPS-text-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("DAPS-Math/DAPS-text-encoder") 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 model.safetensors from DAPS-Math/DAPS-text-encoder: direct link, hf CLI and curl.
- Browser
- Download file 596 MB
-
https://huggingface.co/DAPS-Math/DAPS-text-encoder/resolve/main/model.safetensors
- Command line
-
hf download hf://DAPS-Math/DAPS-text-encoder/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/DAPS-Math/DAPS-text-encoder/resolve/main/model.safetensors
596 MB
- Xet hash:
- c3d4d04521dfb45ace7f82dbc03e778e8d411e8d2363f73f5233f2d518d240fc
- Size of remote file:
- 596 MB
- SHA256:
- d665d03a21ee00d2dfab2ad52f85f1ec829ca2f13ee388d1e0c1d27b7ba7fdf6
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