readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - multi_news
  model-index:
  - name: summarise_v4
    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_v4
  
  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: 2.5264
  - Rouge2 Precision: 0.1349
  - Rouge2 Recall: 0.1187
  - Rouge2 Fmeasure: 0.1227
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
  | 2.9616        | 0.08  | 10   | 2.8008          | 0.0552           | 0.1944        | 0.0844          |
  | 2.7112        | 0.16  | 20   | 2.7017          | 0.1099           | 0.1212        | 0.1078          |
  | 2.6842        | 0.24  | 30   | 2.6653          | 0.119            | 0.1252        | 0.1157          |
  | 2.4638        | 0.32  | 40   | 2.6306          | 0.1386           | 0.1153        | 0.1222          |
  | 2.646         | 0.4   | 50   | 2.6099          | 0.1449           | 0.1095        | 0.122           |
  | 2.5128        | 0.48  | 60   | 2.5945          | 0.1259           | 0.1484        | 0.1313          |
  | 2.6737        | 0.56  | 70   | 2.5832          | 0.1192           | 0.1252        | 0.118           |
  | 2.614         | 0.64  | 80   | 2.5616          | 0.1288           | 0.1179        | 0.1193          |
  | 2.4643        | 0.72  | 90   | 2.5612          | 0.1371           | 0.1227        | 0.124           |
  | 2.3164        | 0.8   | 100  | 2.5606          | 0.1372           | 0.1177        | 0.1223          |
  | 2.4514        | 0.88  | 110  | 2.5339          | 0.1412           | 0.1276        | 0.128           |
  | 2.8113        | 0.96  | 120  | 2.5264          | 0.1349           | 0.1187        | 0.1227          |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.2.dev0
  - Tokenizers 0.12.1
  