Sentence Similarity
sentence-transformers
Safetensors
English
bert
simcse
feature-extraction
contrastive-learning
text-embeddings-inference
Instructions to use Perry-DLC/upy-u2t02-simcse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Perry-DLC/upy-u2t02-simcse with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Perry-DLC/upy-u2t02-simcse") 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 1_Pooling/config.json from Perry-DLC/upy-u2t02-simcse: direct link, hf CLI and curl.
- Browser
- Download file 89 Bytes
-
https://huggingface.co/Perry-DLC/upy-u2t02-simcse/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Perry-DLC/upy-u2t02-simcse/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Perry-DLC/upy-u2t02-simcse/resolve/main/1_Pooling/config.json
89 Bytes
| { | |
| "embedding_dimension": 768, | |
| "pooling_mode": "cls", | |
| "include_prompt": true | |
| } |