readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - multi_news
  model-index:
  - name: summarise_v5
    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. -->
  
  # summarise_v5
  
  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the multi_news dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.3252
  - Rouge2 Precision: 0.1458
  - Rouge2 Recall: 0.1306
  - Rouge2 Fmeasure: 0.1343
  
  ## 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: 5e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
  |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
  | 2.6266        | 0.13  | 10   | 2.4604          | 0.1021           | 0.179         | 0.124           |
  | 2.4818        | 0.27  | 20   | 2.4122          | 0.1402           | 0.1422        | 0.1345          |
  | 2.3451        | 0.4   | 30   | 2.3846          | 0.1631           | 0.1177        | 0.1307          |
  | 2.4462        | 0.53  | 40   | 2.3584          | 0.1671           | 0.1175        | 0.133           |
  | 2.443         | 0.67  | 50   | 2.3395          | 0.1444           | 0.1359        | 0.1344          |
  | 2.3822        | 0.8   | 60   | 2.3377          | 0.1517           | 0.1411        | 0.1395          |
  | 2.4304        | 0.93  | 70   | 2.3252          | 0.1458           | 0.1306        | 0.1343          |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.2.dev0
  - Tokenizers 0.12.1
  