readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - samsum
  metrics:
  - rouge
  model-index:
  - name: flan-t5-base-samsum
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: samsum
        type: samsum
        config: samsum
        split: test
        args: samsum
      metrics:
      - name: Rouge1
        type: rouge
        value: 47.4798
  ---
  
  <!-- 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. -->
  
  # flan-t5-base-samsum
  
  This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the samsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3772
  - Rouge1: 47.4798
  - Rouge2: 23.9756
  - Rougel: 40.0392
  - Rougelsum: 43.6545
  - Gen Len: 17.3162
  
  ## 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: 5e-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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 1.4403        | 1.0   | 1842 | 1.3829          | 46.5346 | 23.1326 | 39.4401 | 42.8272   | 17.0977 |
  | 1.3534        | 2.0   | 3684 | 1.3732          | 47.0911 | 23.5074 | 39.5951 | 43.2279   | 17.4554 |
  | 1.2795        | 3.0   | 5526 | 1.3709          | 46.8895 | 23.3243 | 39.5909 | 43.1286   | 17.2027 |
  | 1.2313        | 4.0   | 7368 | 1.3736          | 47.4946 | 23.7802 | 39.9999 | 43.5903   | 17.2198 |
  | 1.1934        | 5.0   | 9210 | 1.3772          | 47.4798 | 23.9756 | 40.0392 | 43.6545   | 17.3162 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  
  ### Papers With Code Results
  
  As of 2 February 2023 the Papers with Code page for this task has the following leaderboard.
  
  Our score (Rouge 1 score of 47.4798) puts this model's performance between fourth and fifth place on the leaderboard:
  
  
  ![PwC leaderboard](https://i.imgur.com/Nea77uL.jpg)
  
  
  