Sentence Similarity
sentence-transformers
Safetensors
English
Chinese
bert
feature-extraction
test
text-embeddings-inference
Instructions to use 0neisall/tiny-embedding-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use 0neisall/tiny-embedding-test with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("0neisall/tiny-embedding-test") 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 config_sentence_transformers.json from 0neisall/tiny-embedding-test: direct link, hf CLI and curl.
- Browser
- Download file 282 Bytes
-
https://huggingface.co/0neisall/tiny-embedding-test/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://0neisall/tiny-embedding-test/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/0neisall/tiny-embedding-test/resolve/main/config_sentence_transformers.json
282 Bytes
| { | |
| "__version__": { | |
| "pytorch": "2.14.1+cpu", | |
| "sentence_transformers": "5.7.0", | |
| "transformers": "4.57.6" | |
| }, | |
| "default_prompt_name": null, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "document": "", | |
| "query": "" | |
| }, | |
| "similarity_fn_name": "cosine" | |
| } |