Sentence Similarity
sentence-transformers
Safetensors
English
roberta
datadreamer
datadreamer-0.35.0
Synthetic
feature-extraction
text-embeddings-inference
Instructions to use StyleDistance/styledistance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use StyleDistance/styledistance with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("StyleDistance/styledistance") sentences = [ "Did you hear about the Wales wing? He'll h8 2 withdraw due 2 injuries from future competitions.", "We're raising funds 2 improve our school's storage facilities and add new playground equipment!", "Did you hear about the Wales wing? He'll hate to withdraw due to injuries from future competitions." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Inference
- Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from StyleDistance/styledistance: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/StyleDistance/styledistance/resolve/a2ef7ffb93f4af8d9727f65b602e260ba5d13607/1_Pooling/config.json
- Command line
-
hf download hf://StyleDistance/styledistance@a2ef7ffb93f4af8d9727f65b602e260ba5d13607/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/StyleDistance/styledistance/resolve/a2ef7ffb93f4af8d9727f65b602e260ba5d13607/1_Pooling/config.json
296 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |