readme: ---
  license: apache-2.0
  tags:
  - summarization_v1_m5
  - generated_from_trainer
  datasets:
  - samsum
  metrics:
  - rouge
  model-index:
  - name: mt5-small-finetune-sumsum
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: samsum
        type: samsum
        args: samsum
      metrics:
      - name: Rouge1
        type: rouge
        value: 20.9651
  ---
  
  <!-- 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-finetune-sumsum
  
  This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the samsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.3263
  - Rouge1: 20.9651
  - Rouge2: 7.1527
  - Rougel: 18.4396
  - Rougelsum: 19.5209
  
  ## 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: 5.6e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
  | 12.2648       | 1.0   | 125  | 4.3790          | 9.8078  | 1.7255 | 9.0852  | 9.4233    |
  | 6.0853        | 2.0   | 250  | 3.4753          | 20.7185 | 6.502  | 18.079  | 19.2584   |
  | 4.9838        | 3.0   | 375  | 3.3263          | 20.9651 | 7.1527 | 18.4396 | 19.5209   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.13.0
  - Datasets 2.6.1
  - Tokenizers 0.11.0
  