Sentence Similarity
Transformers
PyTorch
ONNX
sentence-transformers
Arabic
bert
feature-extraction
miniDense
passage-retrieval
knowledge-distillation
middle-training
text-embeddings-inference
Instructions to use prithivida/miniDense_arabic_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivida/miniDense_arabic_v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("prithivida/miniDense_arabic_v1") model = AutoModel.from_pretrained("prithivida/miniDense_arabic_v1", device_map="auto") - sentence-transformers
How to use prithivida/miniDense_arabic_v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("prithivida/miniDense_arabic_v1") sentences = [ "هذا شخص سعيد", "هذا كلب سعيد", "هذا شخص سعيد جدا", "اليوم هو يوم مشمس" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -184,14 +184,14 @@ So it makes sense to evaluate our models in retrieval slice of the MTEB benchmar
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##### Long Document Retrieval
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<b><p>Table 3: Detailed Arabic retrieval performance on the MultiLongDoc dev set (measured by nDCG@10)</p></b>
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##### X-lingual Retrieval
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Almost all models below are monolingual arabic models
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<img src="./ar_metrics_5.png" width=80%/>
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##### Long Document Retrieval
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<center>
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<img src="./ar_metrics_4.png" width=150%/>
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<b><p>Table 3: Detailed Arabic retrieval performance on the MultiLongDoc dev set (measured by nDCG@10)</p></b>
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##### X-lingual Retrieval
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Almost all models below are monolingual arabic models so they have no notion of any other languages. But the below table shows how our model excels in cross-lingual scenarios.
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<img src="./ar_metrics_5.png" width=80%/>
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