Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:11180
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Culture-and-Morality-Lab/psyembedding-e5-large-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Culture-and-Morality-Lab/psyembedding-e5-large-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Culture-and-Morality-Lab/psyembedding-e5-large-v2") sentences = [ "Of course she would, otherwise she stands no chance of becoming prez. The only thing above Le Pen's xenophobia is their thrive for power.", "Mormon Church declares same-sex couples apostates and excludes children of those couples from blessings and baptism.", "Feminists have legitimate gripes with the way the world is structured and their ideas are quite sane. The feminists of the world are not deluded or wacky and it's a bad idea to call them that. Open your mind to new ideas and drop the patriarchal thinking.", "Hi, Professor Lichtman. Thanks for doing this AMA. What would you say to people who argue about the economy keys being affected by people not feeling, in terms of their lived experiences, that the economy has been good (due to the cost of living exceeding, in many cases, their income) and this potentially costing Harris the election? They seem to believe that this should be costing her the ST and LT economy keys." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle