Sentence Similarity
sentence-transformers
Safetensors
Transformers
Arabic
bert
feature-extraction
mteb
Generated from Trainer
dataset_size:557850
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka") sentences = [ "ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط النظيفة", "رجل يقدم عرضاً", "هناك رجل بالخارج قرب الشاطئ", "رجل يجلس على أريكه" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka") model = AutoModel.from_pretrained("Omartificial-Intelligence-Space/Arabert-all-nli-triplet-Matryoshka") - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |