readme: ---
  language: "ar"
  tags:
  - text-generation
  datasets:
  - APCD
  widget:
   - text: "."
   - text: "عيد بأية حال"
   - text: "يا قدس"
   - text: "يا قدس"
   - text: "ألا ليت"
  ---
  
  # GPT2-Arabic-Poetry-2023
  
  ## Model description
  
  Fine-tuned model of Arabic poetry dataset based on aragpt2-medium.
  
  ## Intended uses & limitations
  
  #### How to use
  
  Try this [HF Space](https://huggingface.co/spaces/akhooli/poetry).  
  From script: 
  ```
  from transformers import pipeline
  pipe = pipeline('text-generation', framework='pt', device=-1, model='akhooli/ap2023', tokenizer='akhooli/ap2023')
  gen = pipe(prompt, max_length=96, temperature = 0.95,repetition_penalty=1.05,
                 num_beams=3, num_return_sequences=2, do_sample = True, 
                 top_p = 1.0, top_k = 50, return_full_text=True)[0]["generated_text"]
  poetry =""
  for line in gen.split('.')[:-1]:
          poetry += line 
  print(poetry)
  ```
  
  #### Limitations and bias
  
  Both the GPT2-small-arabic (trained on Arabic Wikipedia) and this model have several limitations in terms of coverage and training performance. 
  Use them as demonstrations or proof of concepts but not as production code.
  
  ## Training data
  
  This pretrained model used poems from several eras with a total of around 1.4M lines (1.25M used for training). 
  The dataset was trained (fine-tuned) based on the [aragpt2-medium](https://huggingface.co/aubmindlab/aragpt2-medium) transformer model.
  
  ## Training procedure
  
  Training was done using HF Trainer using free GPU on Kaggle.
  
  ## Eval results 
  Final perplexity reached was 52, eval_accuracy = 0.3704, eval_loss = 3.9513
  
  ### BibTeX entry and citation info
  
  ```bibtex
  @inproceedings{Abed Khooli,
    year={2023}
  }
  ```