readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - scientific_papers
  model-index:
  - name: longformer_summarise
    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. -->
  
  # longformer_summarise
  
  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the scientific_papers dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.3003
  - Rouge2 Precision: 0.1654
  - Rouge2 Recall: 0.0966
  - Rouge2 Fmeasure: 0.1118
  
  ## 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: 2
  - eval_batch_size: 2
  - seed: 42
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
  | 2.909         | 0.08  | 10   | 2.8969          | 0.09             | 0.1439        | 0.0953          |
  | 2.615         | 0.16  | 20   | 2.6182          | 0.1232           | 0.0865        | 0.0924          |
  | 2.581         | 0.24  | 30   | 2.4687          | 0.1357           | 0.0733        | 0.09            |
  | 2.1294        | 0.32  | 40   | 2.5215          | 0.1495           | 0.0932        | 0.1044          |
  | 2.8083        | 0.4   | 50   | 2.3870          | 0.1794           | 0.1054        | 0.1224          |
  | 3.0704        | 0.48  | 60   | 2.3676          | 0.1572           | 0.0989        | 0.1108          |
  | 2.4716        | 0.56  | 70   | 2.3554          | 0.1707           | 0.1039        | 0.1198          |
  | 2.454         | 0.64  | 80   | 2.3411          | 0.1619           | 0.0943        | 0.1115          |
  | 2.3046        | 0.72  | 90   | 2.3105          | 0.1547           | 0.0965        | 0.1116          |
  | 1.7467        | 0.8   | 100  | 2.3417          | 0.1551           | 0.0877        | 0.1046          |
  | 2.7696        | 0.88  | 110  | 2.3226          | 0.1543           | 0.0954        | 0.1085          |
  | 2.4999        | 0.96  | 120  | 2.3003          | 0.1654           | 0.0966        | 0.1118          |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 1.2.1
  - Tokenizers 0.12.1
  