Feature Extraction
sentence-transformers
Safetensors
mpnet
sentence-similarity
Generated from Trainer
dataset_size:6066
loss:OnlineContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use PietroSaveri/meme-cluster-classifier-02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use PietroSaveri/meme-cluster-classifier-02 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PietroSaveri/meme-cluster-classifier-02") sentences = [ "Mitochondria, often called 'powerhouses of the cell,' generate most of the cell's ATP through cellular respiration and have their own DNA.", "Plate tectonics theory explains that Earth's lithosphere is divided into plates that move, causing earthquakes, volcanoes, and mountain formation.", "The Titanic was intentionally sunk as part of an insurance scam by J.P. Morgan.", "Why can't you trust a statistician? They're always plotting something, and they have a mean personality." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle