readme: ---
  license: apache-2.0
  tags:
  - summarization
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: mt5-small-finetunedOn-OrangeSum-PT
    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-small-finetunedOn-OrangeSum-PT
  
  This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.2710
  - Rouge1: 0.2001
  - Rouge2: 0.0581
  - Rougel: 0.1688
  - Rougelsum: 0.1695
  
  ## 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: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
  | 5.6747        | 1.0   | 625  | 2.5273          | 0.1978 | 0.0646 | 0.1656 | 0.1656    |
  | 3.317         | 2.0   | 1250 | 2.4205          | 0.1933 | 0.0548 | 0.1591 | 0.1588    |
  | 3.0952        | 3.0   | 1875 | 2.3493          | 0.1964 | 0.0555 | 0.1613 | 0.1619    |
  | 2.9607        | 4.0   | 2500 | 2.3286          | 0.1927 | 0.0557 | 0.1644 | 0.1657    |
  | 2.8771        | 5.0   | 3125 | 2.3064          | 0.1984 | 0.0559 | 0.1652 | 0.1661    |
  | 2.8192        | 6.0   | 3750 | 2.2847          | 0.1960 | 0.0546 | 0.1641 | 0.1647    |
  | 2.7852        | 7.0   | 4375 | 2.2720          | 0.1989 | 0.0588 | 0.1692 | 0.1701    |
  | 2.7691        | 8.0   | 5000 | 2.2710          | 0.2001 | 0.0581 | 0.1688 | 0.1695    |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.2
  - Tokenizers 0.13.1
  