Sentence Similarity
sentence-transformers
Safetensors
Arabic
Persian
xlm-roberta
embeddings
retrieval
arabic
persian
fiqh
islamic-jurisprudence
cross-lingual
bge-m3
text-embeddings-inference
Instructions to use sadiqoon/fiqh-embed-ar-fa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sadiqoon/fiqh-embed-ar-fa with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sadiqoon/fiqh-embed-ar-fa") 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
| { | |
| "add_prefix_space": true, | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "model_max_length": 8192, | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "XLMRobertaTokenizer", | |
| "unk_token": "<unk>" | |
| } | |