from sentence_transformers import SentenceTransformer

# Load the model
model = SentenceTransformer('hamtaai/bge-m3-hadith')

# For instruct models, use proper prefixes
query = "query: سوال شما اینجا"
passage = "passage: متن پاسخ اینجا"

# Encode texts
query_embedding = model.encode(query)
passage_embedding = model.encode(passage)

# Calculate similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(query_embedding, passage_embedding)

Using Hugging Face Transformers

python

from transformers import AutoTokenizer, AutoModel
import torch

tokenizer = AutoTokenizer.from_pretrained('hamtaai/bge-m3-hadith')
model = AutoModel.from_pretrained('hamtaai/bge-m3-hadith')

# Tokenize and encode
inputs = tokenizer("متن شما", return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1)

Performance

This model has been optimized for Persian and Arabic text processing and shows improved performance on:

Semantic similarity tasks
Question-answering accuracy
Cross-lingual retrieval
Religious text understanding

Training Data

The model was trained on a curated dataset of Persian and Arabic religious texts, including:

Hadith collections
Quranic commentaries (Tafsir)
Religious question-answer pairs
Contextual information for better understanding

Limitations

Primarily optimized for Persian and Arabic texts
Performance may vary on other languages
Best results achieved with proper text normalization
Requires appropriate prefixes for instruct-based models

Citation

If you use this model, please cite the original base model and mention this fine-tuned version:

bibtex

@misc{hamtaai/bge_m3_hadith,
  title={hamtaai/bge-m3-hadith: Fine-tuned Multilingual E5 Model for Persian and Arabic Text Processing},
  author={Your Name},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/hamtaai/bge-m3-hadith}}
}

License

This model is released under the Apache 2.0 License.