readme: ---
  license: apache-2.0
  tags:
  - summarization
  - generated_from_trainer
  datasets:
  - mlsum
  metrics:
  - rouge
  model-index:
  - name: mt5-small-mlsum_training_sample
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: mlsum
        type: mlsum
        config: de
        split: train
        args: de
      metrics:
      - name: Rouge1
        type: rouge
        value: 28.2078
  ---
  
  <!-- 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-mlsum_training_sample
  
  This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the mlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.9727
  - Rouge1: 28.2078
  - Rouge2: 19.0712
  - Rougel: 26.2267
  - Rougelsum: 26.9462
  
  ## 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: 0.001
  - 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: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
  | 1.3193        | 1.0   | 6875  | 2.1352          | 25.8941 | 17.4672 | 24.2858 | 24.924    |
  | 1.2413        | 2.0   | 13750 | 2.0528          | 26.6221 | 18.1166 | 24.8233 | 25.5111   |
  | 1.1844        | 3.0   | 20625 | 1.9783          | 27.0518 | 18.3457 | 25.2288 | 25.8919   |
  | 1.0403        | 4.0   | 27500 | 1.9487          | 27.8154 | 18.9701 | 25.9435 | 26.6578   |
  | 0.9582        | 5.0   | 34375 | 1.9374          | 27.6863 | 18.7723 | 25.7667 | 26.4694   |
  | 0.8992        | 6.0   | 41250 | 1.9353          | 27.8959 | 18.919  | 26.0434 | 26.7262   |
  | 0.8109        | 7.0   | 48125 | 1.9492          | 28.0644 | 18.8873 | 26.0628 | 26.757    |
  | 0.7705        | 8.0   | 55000 | 1.9727          | 28.2078 | 19.0712 | 26.2267 | 26.9462   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  