Text Classification
sentence-transformers
Safetensors
mpnet
sentence-similarity
feature-extraction
Generated from Trainer
dataset_size:991
loss:BatchAllTripletLoss
text-embeddings-inference
Instructions to use auukjkjk/poem_embedding_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use auukjkjk/poem_embedding_model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("auukjkjk/poem_embedding_model") sentences = [ "We pay to enter the dirty pen. We buy small bags of feed to feed the well-fed animals. We are guests in their home, our feet on their sawdust floor. We pretend not to notice the stench. Theirs is a predictable life. Better, I guess, than the slaughter, is the many-handed god. Me? I’m going to leave here, eat a", "Affection", "Death", "Music" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize" | |
| } | |
| ] |