Sentence Similarity
sentence-transformers
PyTorch
Safetensors
Transformers
Arabic
bert
feature-extraction
Hadith
Islam
Arabic
text-embeddings-inference
Instructions to use FDSRashid/QulBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use FDSRashid/QulBERT with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("FDSRashid/QulBERT") 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] - Transformers
How to use FDSRashid/QulBERT with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FDSRashid/QulBERT") model = AutoModel.from_pretrained("FDSRashid/QulBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from FDSRashid/QulBERT: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/FDSRashid/QulBERT/resolve/refs%2Fpr%2F2/modules.json
- Command line
-
hf download hf://FDSRashid/QulBERT@refs/pr/2/modules.json
-
curl -L -o modules.json https://huggingface.co/FDSRashid/QulBERT/resolve/refs%2Fpr%2F2/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |