Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:29450829
loss:CoSENTLoss
text-embeddings-inference
Instructions to use KhaledReda/all-MiniLM-L6-v49-pair_score with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KhaledReda/all-MiniLM-L6-v49-pair_score with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KhaledReda/all-MiniLM-L6-v49-pair_score") sentences = [ "button fastening", "farm", "white suit suits sleeveless suit women 911 suit free size sleeveless white 2 pcs suit sleeveless.", "eye shadow sephora little wishes trio eye palette highly pigmented matte shimmer natural beige brown sephora little wishes eye palette what it is sephora collection limited-edition palette of 3 easy-to-wear universal eye shadows ranging from natural beige to tawny brown and a luminous taupe for a truly feline look what it does their soft silky texture reveals an intense highly pigmented color for an instantly eye-enhancing look. the matte or shimmer finish nude shades are really easy to wear we are sure they will make this palette your everyday go-to selection what else you need to know mini format means you can slip it into your handbag and take it with you wherever you go and a great price" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
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| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
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| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
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| ] |