readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: pegasus-newsroom-cnn1_50k
    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. -->
  
  # pegasus-newsroom-cnn1_50k
  
  This model is a fine-tuned version of [oMateos2020/pegasus-newsroom-cnn1_50k](https://huggingface.co/oMateos2020/pegasus-newsroom-cnn1_50k) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.1267
  - Rouge1: 38.0081
  - Rouge2: 16.5536
  - Rougel: 26.4916
  - Rougelsum: 35.1349
  - Gen Len: 59.4912
  
  ## 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: 0.0001
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 32
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 3.144         | 0.26  | 100  | 3.0323          | 38.3168 | 16.7528 | 26.2646 | 35.2447   | 66.2372 |
  | 3.0556        | 0.51  | 200  | 3.0351          | 38.39   | 16.8027 | 26.3412 | 35.37     | 67.4676 |
  | 3.0701        | 0.77  | 300  | 3.0345          | 38.5742 | 16.922  | 26.3568 | 35.51     | 68.662  |
  | 3.1679        | 1.03  | 400  | 3.0321          | 38.5319 | 16.8049 | 26.4933 | 35.4775   | 65.976  |
  | 3.1041        | 1.28  | 500  | 3.0246          | 38.1381 | 16.63   | 26.2484 | 35.0999   | 64.6896 |
  | 3.0352        | 1.54  | 600  | 3.0206          | 38.9063 | 17.0281 | 27.0288 | 35.9175   | 59.0668 |
  | 3.0894        | 1.79  | 700  | 3.0251          | 38.4461 | 16.7732 | 26.4394 | 35.4807   | 63.2792 |
  | 3.0529        | 2.05  | 800  | 3.0400          | 38.5088 | 16.8921 | 26.5526 | 35.5236   | 64.294  |
  | 3.0002        | 2.31  | 900  | 3.0394          | 38.6899 | 16.8703 | 26.6771 | 35.6207   | 62.8004 |
  | 3.0167        | 2.56  | 1000 | 3.0394          | 38.3532 | 16.6176 | 26.5433 | 35.3282   | 61.63   |
  | 3.0168        | 2.82  | 1100 | 3.0421          | 38.7613 | 17.0107 | 26.8424 | 35.7508   | 62.67   |
  | 3.0412        | 3.08  | 1200 | 3.0491          | 38.6132 | 16.8046 | 26.61   | 35.6002   | 61.7924 |
  | 3.1273        | 3.33  | 1300 | 3.0823          | 38.5498 | 16.795  | 26.5569 | 35.613    | 60.6872 |
  | 3.0634        | 3.59  | 1400 | 3.1010          | 38.0927 | 16.4367 | 26.2315 | 35.1311   | 59.252  |
  | 3.097         | 3.84  | 1500 | 3.1147          | 37.7644 | 16.3156 | 26.2674 | 34.8315   | 59.7592 |
  | 3.1287        | 4.1   | 1600 | 3.1267          | 38.0081 | 16.5536 | 26.4916 | 35.1349   | 59.4912 |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  