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 modules.json from AhmedZaky1/DIMI-embedding-v2: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/AhmedZaky1/DIMI-embedding-v2/resolve/main/modules.json
- Command line
-
hf download hf://AhmedZaky1/DIMI-embedding-v2/modules.json
-
curl -L -o modules.json https://huggingface.co/AhmedZaky1/DIMI-embedding-v2/resolve/main/modules.json
349 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |