readme: ---
  license: apache-2.0
  tags:
  - summarization
  - generated_from_trainer
  datasets:
  - xlsum
  metrics:
  - rouge
  model-index:
  - name: mt5-small-finetuned-mt5-en
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: xlsum
        type: xlsum
        args: english
      metrics:
      - name: Rouge1
        type: rouge
        value: 23.8952
  ---
  
  <!-- 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-small-finetuned-mt5-en
  
  This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.8345
  - Rouge1: 23.8952
  - Rouge2: 5.8792
  - Rougel: 18.6495
  - Rougelsum: 18.7057
  
  ## 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: 10
  - total_train_batch_size: 40
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 100
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
  | No log        | 1.0   | 224  | 3.0150          | 24.4639 | 5.3016 | 18.3987 | 18.4963   |
  | No log        | 2.0   | 448  | 2.8738          | 24.5075 | 5.842  | 18.8133 | 18.9072   |
  | No log        | 3.0   | 672  | 2.8345          | 23.8952 | 5.8792 | 18.6495 | 18.7057   |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  