readme: ---
  license: apache-2.0
  tags:
  - summarization
  - english
  - en
  - mt5
  - Abstractive Summarization
  - generated_from_trainer
  datasets:
  - xlsum
  model-index:
  - name: mt5-base-finetuned-english
    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. -->
  
  # mt5-base-finetuned-english
  
  This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the xlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.3271
  - Rouge-1: 31.7
  - Rouge-2: 11.83
  - Rouge-l: 26.43
  - Gen Len: 18.88
  - Bertscore: 74.3
  
  ## 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: 0.0005
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 4.174         | 1.0   | 3125  | 3.5662          | 27.01   | 7.95    | 22.16   | 18.91   | 72.62     |
  | 3.6577        | 2.0   | 6250  | 3.4304          | 28.84   | 9.09    | 23.64   | 18.87   | 73.32     |
  | 3.4526        | 3.0   | 9375  | 3.3691          | 29.69   | 9.96    | 24.58   | 18.84   | 73.69     |
  | 3.3091        | 4.0   | 12500 | 3.3368          | 30.38   | 10.32   | 25.1    | 18.9    | 73.9      |
  | 3.2056        | 5.0   | 15625 | 3.3271          | 30.7    | 10.65   | 25.45   | 18.89   | 73.99     |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.0
  - Tokenizers 0.12.1
  