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 sentence_bert_config.json from bau0221/ptz_embedding: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/bau0221/ptz_embedding/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://bau0221/ptz_embedding/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/bau0221/ptz_embedding/resolve/main/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 512, | |
| "do_lower_case": false | |
| } |