readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - orange_sum
  metrics:
  - rouge
  model-index:
  - name: bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: orange_sum
        type: orange_sum
        args: abstract
      metrics:
      - name: Rouge1
        type: rouge
        value: 24.949
  ---
  
  <!-- 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. -->
  
  <img src="https://huggingface.co/Chemsseddine/bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new/resolve/main/logobert2gpt2.png" alt="Map of positive probabilities per country." width="200"/>
  
  # bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum
  
  This model is a fine-tuned version of [Chemsseddine/bert2gpt2SUMM-finetuned-mlsum](https://huggingface.co/Chemsseddine/bert2gpt2SUMM-finetuned-mlsum) on the orange_sum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.1773
  - Rouge1: 24.949
  - Rouge2: 7.851
  - Rougel: 18.1575
  - Rougelsum: 18.4114
  - Gen Len: 39.7947
  
  ## 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: 16
  - eval_batch_size: 16
  - 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 | Rouge1 | Rouge2 | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:|
  | 3.5484        | 1.0   | 1338 | 3.1773          | 24.949 | 7.851  | 18.1575 | 18.4114   | 39.7947 |
  
  
  ### Framework versions
  
  - Transformers 4.20.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  