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
File size: 284 Bytes
cfbd59a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"__version__": {
"sentence_transformers": "5.2.0",
"transformers": "4.57.3",
"pytorch": "2.7.0+cu128"
},
"prompts": {
"query": "",
"document": ""
},
"default_prompt_name": null,
"similarity_fn_name": "cosine",
"model_type": "SentenceTransformer"
}
|