Sentence Similarity
sentence-transformers
Safetensors
Arabic
English
new
feature-extraction
Generated from Trainer
dataset_size:34436
loss:MatryoshkaLoss
loss:CoSENTLoss
custom_code
text-embeddings-inference
Instructions to use AhmedZaky1/DIMI-embedding-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AhmedZaky1/DIMI-embedding-v4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AhmedZaky1/DIMI-embedding-v4", trust_remote_code=True) sentences = [ "الرجل يركب حصاناً", "رجل يُبث الجبن الممزق على البيتزا" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [2, 2] - Notebooks
- Google Colab
- Kaggle
Download modules.json from AhmedZaky1/DIMI-embedding-v4: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/AhmedZaky1/DIMI-embedding-v4/resolve/main/modules.json
- Command line
-
hf download hf://AhmedZaky1/DIMI-embedding-v4/modules.json
-
curl -L -o modules.json https://huggingface.co/AhmedZaky1/DIMI-embedding-v4/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" | |
| } | |
| ] |