Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:126423
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use codersan/newfa_e5base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codersan/newfa_e5base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codersan/newfa_e5base") sentences = [ "چگونه باید درست از سال اول آماده شوم تا Google Summer of Code را ترک کنم؟", "یک پروژه ترم خوب برای یک دوره تجزیه و تحلیل مدار چیست؟", "چگونه می توانم تابستان کد GSOC-Google را ترک کنم؟", "یک بازیکن فوتبال در حال پوشیدن بازوبندهای مشکی است" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 900d575674d5bfcd5c2b9059c9221a42f92c957ed7230f7e096d3e9580c3e4c2
- Size of remote file:
- 17.1 MB
- SHA256:
- 883b037111086fd4dfebbbc9b7cee11e1517b5e0c0514879478661440f137085
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