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
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "full_tokenizer_file": null, "name_or_path": "CAMeL-Lab/bert-base-arabic-camelbert-ca", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |