Sentence Similarity
sentence-transformers
Safetensors
clip
feature-extraction
Generated from Trainer
dataset_size:12
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use machinev/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use machinev/model with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("machinev/model") sentences = [ "the main power cable is connected with LPT ", "the main power cable is connected with LPT ", "the main power cable is connected with LPT ", "/content/sample_data/images/LPT (2).jpeg" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e43f91e2544861d87ccbbe1aa5c639f8fd85b3caa9e3026b5c1001cca4971634
- Size of remote file:
- 1.71 GB
- SHA256:
- 90ffdd36f1d515660008a6e288ae6e9b51401ae65daadc06a86215ac360b8269
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