Sentence Similarity
sentence-transformers
PyTorch
Transformers
roberta
feature-extraction
text-embeddings-inference
Instructions to use AnnaWegmann/Style-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AnnaWegmann/Style-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AnnaWegmann/Style-Embedding") 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] - Transformers
How to use AnnaWegmann/Style-Embedding with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AnnaWegmann/Style-Embedding") model = AutoModel.from_pretrained("AnnaWegmann/Style-Embedding") - Inference
- Notebooks
- Google Colab
- Kaggle
| epoch,steps,accuracy_cosinus,accuracy_manhatten,accuracy_euclidean | |
| 0,500,0.5233333333333333,0.5203111111111111,0.5210222222222223 | |
| 0,1000,0.5386666666666666,0.5338222222222222,0.5371777777777778 | |
| 0,1500,0.5505333333333333,0.5537333333333333,0.5490888888888888 | |
| 0,2000,0.5679555555555555,0.5611555555555555,0.5624 | |
| 0,2500,0.5904,0.5834222222222222,0.5845111111111111 | |
| 0,3000,0.6135111111111111,0.6053555555555555,0.6076 | |
| 0,3500,0.6204888888888889,0.6115111111111111,0.6148666666666667 | |
| 0,4000,0.6232,0.6096666666666667,0.6149333333333333 | |
| 0,4500,0.6252,0.612,0.6158666666666667 | |
| 0,5000,0.6259555555555556,0.6150444444444444,0.6181777777777778 | |
| 0,5500,0.6288,0.617,0.621 | |