Sentence Similarity
sentence-transformers
Safetensors
new
feature-extraction
Generated from Trainer
dataset_size:498670
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use AhmedZaky1/DIMI-embedding-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AhmedZaky1/DIMI-embedding-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AhmedZaky1/DIMI-embedding-v2", trust_remote_code=True) sentences = [ "كم يبغ عدد السكان في المملكة المتحدة؟", "هناك العديد من الناس الحاضرين.", "كم عدد سكان أوكرانيا؟", "لماذا باراك أوباما غير مؤهل للترشح في انتخابات الرئاسة لعام 2016؟" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from AhmedZaky1/DIMI-embedding-v2: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/AhmedZaky1/DIMI-embedding-v2/resolve/main/tokenizer.json
- Command line
-
hf download hf://AhmedZaky1/DIMI-embedding-v2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/AhmedZaky1/DIMI-embedding-v2/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- c61862a1d9f43a3f76eae5cb18b43fddddc1277bec41bbc0aa8df16676ee449d
- Size of remote file:
- 17.1 MB
- SHA256:
- aa7a6ad87a7ce8fe196787355f6af7d03aee94d19c54a5eb1392ed18c8ef451a
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