readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: summarise_v8
    results: []
  ---
  ![SGH logo.png](https://s3.amazonaws.com/moonup/production/uploads/1667143139655-631feef1124782a19eff4243.png)
  
  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the SGH news articles and summaries dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8163
  - Rouge2 Precision: 0.3628
  - Rouge2 Recall: 0.3589
  - Rouge2 Fmeasure: 0.3316
  
  ## Model description
  
  This model was created to generate summaries of news articles.
  
  ## Intended uses & limitations
  
  The model takes up to maximum article length of 768 tokens and generates a summary of maximum length of 512 tokens, and minimum length of 100 tokens.
  
  ## Training and evaluation data
  
  This model was trained on 100+ articles and summaries from SGH.
  
  ### 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 |
  |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
  | 1.5952        | 0.23  | 10   | 1.0414          | 0.2823           | 0.3908        | 0.3013          |
  | 1.8116        | 0.47  | 20   | 0.9171          | 0.3728           | 0.273         | 0.3056          |
  | 1.6289        | 0.7   | 30   | 0.8553          | 0.3284           | 0.2892        | 0.291           |
  | 1.5074        | 0.93  | 40   | 0.8163          | 0.3628           | 0.3589        | 0.3316          |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 1.2.1
  - Tokenizers 0.12.1
  