readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - cnn_dailymail
  model-index:
  - name: roberta_gpt2_summarization_cnn_dailymail
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # roberta_gpt2_summarization_cnn_dailymail
  
  This model is a fine-tuned version of [](https://huggingface.co/) on the cnn_dailymail dataset.
  
  ## Model description
  This model uses RoBerta encoder and GPT2 decoder and fine-tuned on the summarization task. It got Rouge scores as follows:
  
  Rouge1= 35.886
  
  Rouge2= 16.292
  
  RougeL= 23.499
  ## Intended uses & limitations
  To use its API:
  
  from transformers import RobertaTokenizerFast, GPT2Tokenizer, EncoderDecoderModel
  
  model = EncoderDecoderModel.from_pretrained("Ayham/roberta_gpt2_summarization_cnn_dailymail")
  
  input_tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base')
  
  output_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
  
  article = """Your Input Text"""
  
  input_ids = input_tokenizer(article, return_tensors="pt").input_ids
  
  output_ids = model.generate(input_ids)
  
  print(output_tokenizer.decode(output_ids[0], skip_special_tokens=True))
  
  More information needed
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 2000
  - num_epochs: 3.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.12.0.dev0
  - Pytorch 1.10.0+cu111
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  