readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: model2-bart-reverse
    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. -->
  
  # model2-bart-reverse
  
  This model is a fine-tuned version of [theojolliffe/model-2-bart-reverse-raw](https://huggingface.co/theojolliffe/model-2-bart-reverse-raw) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5768
  - Rouge1: 50.0024
  - Rouge2: 44.5149
  - Rougel: 50.408
  - Rougelsum: 50.0015
  - Gen Len: 20.0
  
  ## 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: 2e-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: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | No log        | 1.0   | 160  | 0.5107          | 54.2246 | 50.7271 | 51.1954 | 54.4944   | 20.0    |
  | No log        | 2.0   | 320  | 0.5895          | 48.1317 | 43.4207 | 48.6594 | 48.4308   | 20.0    |
  | No log        | 3.0   | 480  | 0.5833          | 51.7747 | 46.8312 | 52.47   | 52.1239   | 20.0    |
  | 0.4286        | 4.0   | 640  | 0.5768          | 50.0024 | 44.5149 | 50.408  | 50.0015   | 20.0    |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  