readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: summarise_v11
    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_v11
  
  This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.6322
  - Rouge1 Precision: 0.6059
  - Rouge1 Recall: 0.6233
  - Rouge1 Fmeasure: 0.5895
  - Rouge2 Precision: 0.4192
  - Rouge2 Recall: 0.4512
  - Rouge2 Fmeasure: 0.4176
  - Rougel Precision: 0.4622
  - Rougel Recall: 0.4946
  - Rougel Fmeasure: 0.4566
  - Rougelsum Precision: 0.4622
  - Rougelsum Recall: 0.4946
  - Rougelsum Fmeasure: 0.4566
  
  ## 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
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure |
  |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|
  | 1.6201        | 0.45  | 10   | 1.4875          | 0.3203           | 0.64          | 0.3932          | 0.197            | 0.3839        | 0.2385          | 0.1952           | 0.4051        | 0.2454          | 0.1952              | 0.4051           | 0.2454             |
  | 0.9172        | 0.91  | 20   | 1.4404          | 0.4917           | 0.5134        | 0.4699          | 0.288            | 0.3095        | 0.276           | 0.3371           | 0.3594        | 0.3277          | 0.3371              | 0.3594           | 0.3277             |
  | 1.0923        | 1.36  | 30   | 1.3575          | 0.519            | 0.5505        | 0.4936          | 0.3114           | 0.3237        | 0.2958          | 0.3569           | 0.3702        | 0.3364          | 0.3569              | 0.3702           | 0.3364             |
  | 1.1287        | 1.82  | 40   | 1.3269          | 0.4913           | 0.5997        | 0.5068          | 0.3108           | 0.3964        | 0.3269          | 0.3355           | 0.427         | 0.3521          | 0.3355              | 0.427            | 0.3521             |
  | 0.9938        | 2.27  | 50   | 1.3189          | 0.5339           | 0.5781        | 0.4973          | 0.3555           | 0.3883        | 0.3345          | 0.3914           | 0.4289        | 0.3678          | 0.3914              | 0.4289           | 0.3678             |
  | 0.8659        | 2.73  | 60   | 1.3241          | 0.525            | 0.638         | 0.5165          | 0.3556           | 0.4349        | 0.3535          | 0.3914           | 0.4793        | 0.3886          | 0.3914              | 0.4793           | 0.3886             |
  | 0.6187        | 3.18  | 70   | 1.3360          | 0.5875           | 0.5864        | 0.5416          | 0.4005           | 0.4045        | 0.3701          | 0.4485           | 0.4556        | 0.414           | 0.4485              | 0.4556           | 0.414              |
  | 0.3941        | 3.64  | 80   | 1.4176          | 0.5373           | 0.6415        | 0.5328          | 0.3576           | 0.446         | 0.3642          | 0.3787           | 0.4586        | 0.3781          | 0.3787              | 0.4586           | 0.3781             |
  | 0.4145        | 4.09  | 90   | 1.3936          | 0.4127           | 0.6553        | 0.4568          | 0.2568           | 0.4498        | 0.2988          | 0.2918           | 0.4933        | 0.328           | 0.2918              | 0.4933           | 0.328              |
  | 0.4203        | 4.55  | 100  | 1.4703          | 0.6545           | 0.601         | 0.5981          | 0.4789           | 0.4373        | 0.438           | 0.5251           | 0.4851        | 0.4818          | 0.5251              | 0.4851           | 0.4818             |
  | 0.687         | 5.0   | 110  | 1.4304          | 0.5566           | 0.6357        | 0.5637          | 0.3734           | 0.4186        | 0.3748          | 0.4251           | 0.4825        | 0.4286          | 0.4251              | 0.4825           | 0.4286             |
  | 0.4006        | 5.45  | 120  | 1.5399          | 0.5994           | 0.5794        | 0.5515          | 0.4215    