readme: ---
  language: fr
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: bert2gpt2_med_ml_orange_summ-finetuned_med_sum_new-finetuned_med_sum_new
    results: []
  ---
  <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
  
  ---
  ## This model is used for french summarization
  - Problem type: Summarization
  - Model ID: 980832493
  - CO2 Emissions (in grams): 0.10685501288084795
  
  This model is a fine-tuned version of [Chemsseddine/bert2gpt2SUMM](https://huggingface.co/Chemsseddine/bert2gpt2SUMM) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.03749418258667
  - Rouge1: 28.8384
  - Rouge2: 10.7511
  - RougeL: 27.0842
  - RougeLsum: 27.5118
  - Gen Len: 22.0625
  
  
  ### 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: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | No log        | 1.0   | 33199  | 4.03749       | 28.8384 | 10.7511 | 27.0842 | 27.5118   | 22.0625 |
  
  