Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:710
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use bau0221/ptz_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bau0221/ptz_embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bau0221/ptz_embedding") sentences = [ "Set Camera 4 to follow Ava at the top side", "Camera 4 put Grace on the top side", "Set Camera 3 to put Michael at the bottom side", "Set Wyatt at the left side on group1" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download config_sentence_transformers.json from bau0221/ptz_embedding: direct link, hf CLI and curl.
- Browser
- Download file 205 Bytes
-
https://huggingface.co/bau0221/ptz_embedding/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://bau0221/ptz_embedding/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/bau0221/ptz_embedding/resolve/main/config_sentence_transformers.json
205 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "3.3.1", | |
| "transformers": "4.47.1", | |
| "pytorch": "2.5.1+cu121" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |