Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:685672
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use AhmedZaky1/DIMI-embedding-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use AhmedZaky1/DIMI-embedding-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("AhmedZaky1/DIMI-embedding-v3") sentences = [ "كيف يمكنني اختراق أو التجسس على محادثة WhatsApp لشخص ما عن بعد؟", "كيف يمكنني اختراق حساب واتس اب لشخص ما؟", "ما هو معنى الحياة؟", "ولاقت الاتفاقية ترحيب أعضاء آخرين من عصبة الأمم." ] 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-v3: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/AhmedZaky1/DIMI-embedding-v3/resolve/main/modules.json
- Command line
-
hf download hf://AhmedZaky1/DIMI-embedding-v3/modules.json
-
curl -L -o modules.json https://huggingface.co/AhmedZaky1/DIMI-embedding-v3/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" | |
| } | |
| ] |