Sentence Similarity
sentence-transformers
Safetensors
distilbert
feature-extraction
dense
Generated from Trainer
dataset_size:3810
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use SwarajSolanke-turtle/make-model-SimCSE-embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SwarajSolanke-turtle/make-model-SimCSE-embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SwarajSolanke-turtle/make-model-SimCSE-embedding") sentences = [ "bmw ix", "bmw ix", "bmw x7", "bmw i5" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Commit History
Yesterday
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Unsupervised SimCSE on FW make/model strings (distilbert-base-uncased, bs30)
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