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
File size: 313 Bytes
cd0ea2f | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 1024,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": true,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": false,
"include_prompt": true
} |