Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:3465
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use consight/consight-embeddings-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use consight/consight-embeddings-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("consight/consight-embeddings-v2") sentences = [ "CMU walls (WB epoxy topcoat) - scrubber dump walls", "CMU walls WB epoxy topcoat scrubber dump walls", "digital mCP sErCo mOuntEd By gc eLectRicIAN", "MV 5witchgear 15KV MV Meta1 Clad Lead time 52 weeks" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle