Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:166088
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/multilingual-e5-base-Fa-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/multilingual-e5-base-Fa-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/multilingual-e5-base-Fa-v3") sentences = [ "کدام یک از تجربیات بدی که در زندگی داشتید؟", "چگونه برای اولین بار با پورنو آشنا شدید؟", "آیا Urjit Patel برای فرماندار RBI مناسب است؟", "برخی از تجربیات خوب و بد زندگی شما چه بود؟" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ef8fd2e32f25a3a5efdcff24f72a077e8ea2c747860df7554e9cff1e0c0ec4e
- Size of remote file:
- 1.11 GB
- SHA256:
- 017b133c2bfa9025b0922e12d3ef2aba3eb5a85551e1645e7e58c97b617aad4c
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