Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:2991559
loss:CachedMultiPositiveRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use mjaliz/bslm-pair-original-multipositive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mjaliz/bslm-pair-original-multipositive with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mjaliz/bslm-pair-original-multipositive") sentences = [ "کوله پشتی صندوقی پسرانه", "q-d6abe050cc50d858b466b2cb", "کوله پشتی صندوقی پسرانه طرح فضانورد وارداتی", "p-26697244", "کولهپشتی صندوقی پسرانه طرح فضانورد مدل وارداتی رنگ سرمهای" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [5, 5] - Notebooks
- Google Colab
- Kaggle
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