Sentence Similarity
sentence-transformers
Safetensors
English
deberta-v2
feature-extraction
naturalness
contrastive-learning
ordinal-regression
deberta
text-embeddings-inference
Instructions to use foudil/lens-naturalness-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use foudil/lens-naturalness-encoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("foudil/lens-naturalness-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
- Xet hash:
- 6bd079d8585fbcdbf47cb3a534f888b5aaa32b8994301f5c47573e6cf825d7da
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.