readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - pub_med_summarization_dataset
  metrics:
  - rouge
  model-index:
  - name: wikihow-t5-small-finetuned-pubmed
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: pub_med_summarization_dataset
        type: pub_med_summarization_dataset
        args: document
      metrics:
      - name: Rouge1
        type: rouge
        value: 8.9619
  ---
  
  <!-- 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. -->
  
  # wikihow-t5-small-finetuned-pubmed
  
  This model is a fine-tuned version of [deep-learning-analytics/wikihow-t5-small](https://huggingface.co/deep-learning-analytics/wikihow-t5-small) on the pub_med_summarization_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.2702
  - Rouge1: 8.9619
  - Rouge2: 3.2719
  - Rougel: 8.1558
  - Rougelsum: 8.5714
  - Gen Len: 19.0
  
  ## 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: 2e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
  | 2.5984        | 1.0   | 4000  | 2.3696          | 10.237 | 3.8609 | 8.9776 | 9.677     | 19.0    |
  | 2.5677        | 2.0   | 8000  | 2.3132          | 9.302  | 3.4499 | 8.3816 | 8.8831    | 19.0    |
  | 2.5038        | 3.0   | 12000 | 2.2884          | 9.0578 | 3.3103 | 8.23   | 8.6723    | 19.0    |
  | 2.4762        | 4.0   | 16000 | 2.2758          | 9.0001 | 3.2882 | 8.1845 | 8.6084    | 19.0    |
  | 2.4393        | 5.0   | 20000 | 2.2702          | 8.9619 | 3.2719 | 8.1558 | 8.5714    | 19.0    |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.6
  