readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: bert2gpt2_med_v2 
    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. -->
  
  <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"/>
  
  # bert2gpt2_med_v2 
  
  This model is a fine-tuned version of [Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum](https://huggingface.co/Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.0684
  - Rouge1: 34.1248
  - Rouge2: 17.7006
  - Rougel: 33.4661
  - Rougelsum: 33.4419
  - Gen Len: 22.6429
  
  ## 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: 1
  - eval_batch_size: 1
  - 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  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 2.9107        | 1.0   | 1000  | 2.0877          | 30.4547 | 14.4024 | 30.3642 | 30.3788   | 21.9714 |
  | 1.8782        | 2.0   | 2000  | 1.8151          | 32.6607 | 16.8089 | 32.3844 | 32.4762   | 21.7714 |
  | 1.291         | 3.0   | 3000  | 1.7523          | 33.6391 | 16.7866 | 32.4256 | 32.3306   | 22.7429 |
  | 0.819         | 4.0   | 4000  | 1.7650          | 35.0633 | 19.1222 | 34.4902 | 34.6796   | 22.4714 |
  | 0.4857        | 5.0   | 5000  | 1.8129          | 33.8763 | 16.9303 | 32.8845 | 32.9225   | 22.3857 |
  | 0.3232        | 6.0   | 6000  | 1.9339          | 33.9272 | 17.1784 | 32.9301 | 33.0253   | 22.4286 |
  | 0.2022        | 7.0   | 7000  | 1.9634          | 33.9869 | 16.4238 | 33.7336 | 33.65     | 22.6429 |
  | 0.1452        | 8.0   | 8000  | 2.0090          | 33.8892 | 18.2723 | 33.7514 | 33.6531   | 22.5714 |
  | 0.0845        | 9.0   | 9000  | 2.0337          | 33.9649 | 17.1339 | 33.5061 | 33.4157   | 22.7857 |
  | 0.0531        | 10.0  | 10000 | 2.0684          | 34.1248 | 17.7006 | 33.4661 | 33.4419   | 22.6429 |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  