Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:269012
loss:CoSENTLoss
text-embeddings-inference
Instructions to use IshTale/EccomerceEmbeddingModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use IshTale/EccomerceEmbeddingModel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("IshTale/EccomerceEmbeddingModel") sentences = [ "smart cutting machine for crafts", "HyperX Cloud Alpha Wireless Gaming Headset", "Rubbermaid Brilliance 20-Piece Food Storage Set", "Men's Wick Short Sleeve Crew - Light Merino Wool Camo Hunting Shirt, UV Protection Moisture Management Base Layer" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c03316f037ad2d61ccb3e7ae79fe8f782e4149a630ab6c2200b2f86bc8bc03e1
- Size of remote file:
- 1.34 GB
- SHA256:
- d564b6d5cc06f103e53f64938f6f6f17d2696b690a5ff39031722ed0861192d4
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