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:
# 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 1_Pooling/config.json from FDSRashid/QulBERT: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/FDSRashid/QulBERT/resolve/299e990b381300825bba7cf09a2baecdd3d1df80/1_Pooling/config.json
- Command line
-
hf download hf://FDSRashid/QulBERT@299e990b381300825bba7cf09a2baecdd3d1df80/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/FDSRashid/QulBERT/resolve/299e990b381300825bba7cf09a2baecdd3d1df80/1_Pooling/config.json
190 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": true, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |