Sentence Similarity
sentence-transformers
TensorBoard
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:21484
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/validadted_e5Fa_onV8d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/validadted_e5Fa_onV8d with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/validadted_e5Fa_onV8d") sentences = [ "زنی ماهی را سرخ می کند.", "ماهی توسط زنی پخته می شود", "در سال ۱۱۵۷ ق.م کوتیر-ناهوته حکمران ایلام برای گرفتن انتقام بابل را فتح میکند.", "دو نفر سوار موتورسیکلت می شوند" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e4a965a8f26581337da08881d2e33ed9188dc85ab30448081bdf0cf83ed482d
- Size of remote file:
- 5.62 kB
- SHA256:
- edf653cba2e0838bafe57ed721f7260d28b9adcb1365f8e9053ac4f6461adb8c
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