readme: ---
  tags:
  - summarization
  - en
  - seq2seq
  - mbart
  - Abstractive Summarization
  - generated_from_trainer
  datasets:
  - cnn_dailymail
  model-index:
  - name: mbert-finetune-en-cnn
    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. -->
  
  # mbert-finetune-en-cnn
  
  This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the cnn_dailymail dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.5577
  - Rouge-1: 37.69
  - Rouge-2: 16.47
  - Rouge-l: 35.53
  - Gen Len: 79.93
  - Bertscore: 74.92
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0005
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 250
  - num_epochs: 5
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.20.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  