readme: ---
  language: 
    - fa
  tags:
    - Wikipedia
    - Summarizer
    - bert2bert
    - Summarization
  task_categories:
    - Summarization
    - text generation
  task_ids:
  - news-articles-summarization
  license:
  - apache-2.0
  multilinguality:
  - monolingual
  datasets:
  - pn-summary
  - XL-Sum
  metrics:
  - rouge-1
  - rouge-2
  - rouge-l
  ---
  
  
  # WikiBert2WikiBert
  Bert language models can be employed for Summarization tasks. WikiBert2WikiBert is an encoder-decoder transformer model that is initialized using the Persian WikiBert Model weights. The WikiBert Model is a Bert language model which is fine-tuned on Persian Wikipedia. After using the WikiBert weights for initialization, the model is trained for five epochs on PN-summary and Persian BBC datasets.
  
  ## How to Use:
  You can use the code below to get the model's outputs, or you can simply use the demo on the right.
  ```
  from transformers import (
      BertTokenizerFast,
      EncoderDecoderConfig,
      EncoderDecoderModel,
      BertConfig
  )
  
  model_name = 'Arashasg/WikiBert2WikiBert'
  tokenizer = BertTokenizerFast.from_pretrained(model_name)
  config = EncoderDecoderConfig.from_pretrained(model_name)
  model = EncoderDecoderModel.from_pretrained(model_name, config=config)
  
  
  def generate_summary(text):
      inputs = tokenizer(text, padding="max_length", truncation=True, max_length=512, return_tensors="pt")
      input_ids = inputs.input_ids.to("cuda")
      attention_mask = inputs.attention_mask.to("cuda")
  
      outputs = model.generate(input_ids, attention_mask=attention_mask)
  
      output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)
  
  
      return output_str
  
  input = 'your input comes here'
  summary = generate_summary(input)
  ```
  
  ## Evaluation
  I separated 5 percent of the pn-summary for evaluation of the model. The rouge scores of the model are as follows:
  
  | Rouge-1  | Rouge-2  | Rouge-l |
  | ------------- | ------------- | ------------- |
  | 38.97%  | 18.42%  | 34.50%  |
  
  