# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("pourmand1376/arabic-quran-nahj-sahife")
model = AutoModelForMaskedLM.from_pretrained("pourmand1376/arabic-quran-nahj-sahife", device_map="auto")Quick Links
A model which is jointly trained and fine-tuned on Quran, Saheefa and nahj-al-balaqa. All Datasets are available Here. Code will be available soon ...
Some Examples for filling the mask:
ุฐููููู [MASK] ููุง ุฑูููุจู ููููู ููุฏูู ููููู ูุชููููููู
- ```
ููุง ุฃููููููุง ุงููููุงุณู ุงุนูุจูุฏููุง ุฑูุจููููู
ู ุงูููุฐูู ุฎูููููููู
ู ููุงูููุฐูููู ู
ููู ููุจูููููู
ู ููุนููููููู
ู [MASK]
This model is fine-tuned on Bert Base Arabic for 30 epochs. We have used Masked Language Modeling to fine-tune the model. Also, after each 5 epochs, we have completely masked the words again for the model to learn the embeddings very well and not overfit the data.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pourmand1376/arabic-quran-nahj-sahife")