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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AnnaWegmann/Style-Embedding") model = AutoModel.from_pretrained("AnnaWegmann/Style-Embedding", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AnnaWegmann/Style-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/AnnaWegmann/Style-Embedding/resolve/refs%2Fpr%2F7/pytorch_model.bin
- Command line
-
hf download hf://AnnaWegmann/Style-Embedding@refs/pr/7/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AnnaWegmann/Style-Embedding/resolve/refs%2Fpr%2F7/pytorch_model.bin
499 MB
- Xet hash:
- 82193e1dce5069b5f4e418be06948c3b0e4502ea3c398d55ebca9322e42c3e2a
- Size of remote file:
- 499 MB
- SHA256:
- 3186cd80660a7169a911bace4d54416cf5771a319a22f84c3a79a961ecb0c6f5
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