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
Download modules.json from prithivida/miniDense_arabic_v1: direct link, hf CLI and curl.
- Browser
- Download file 349 Bytes
-
https://huggingface.co/prithivida/miniDense_arabic_v1/resolve/main/modules.json
- Command line
-
hf download hf://prithivida/miniDense_arabic_v1/modules.json
-
curl -L -o modules.json https://huggingface.co/prithivida/miniDense_arabic_v1/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" | |
| } | |
| ] |