readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - summarization
  - stacked summaries
  - prompt engineering
  metrics:
  - rouge
  datasets:
  - stacked-summaries/stacked-samsum-1024
  model-index:
  - name: flan-t5-large-stacked-samsum1024-WIP3
    results:
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: samsum
        type: samsum
        config: samsum
        split: test
      metrics:
      - name: ROUGE-1
        type: rouge
        value: 47.6682
        verified: true
      - name: ROUGE-2
        type: rouge
        value: 23.3053
        verified: true
      - name: ROUGE-L
        type: rouge
        value: 39.7678
        verified: true
      - name: ROUGE-LSUM
        type: rouge
        value: 43.259
        verified: true
      - name: loss
        type: loss
        value: 2.372586965560913
        verified: true
      - name: gen_len
        type: gen_len
        value: 17.4237
        verified: true
  language:
  - en
  library_name: transformers
  pipeline_tag: summarization
  widget:
  parameters:
    max_length: 128
    min_length: 4
    num_beams: 8
    repetition_penalty: 1.21
    length_penalty: 1
    early_stopping: true
  ---
  
  
  # flan-t5-large-stacked-samsum-1024
  
   <a href="https://colab.research.google.com/gist/pszemraj/a4bf61f593ebda9a8db6dc58839d9de4/brief-demo-flan-t5-stacked-samsum.ipynb">
    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
  </a>
  
  This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the `stacked-summaries/stacked-samsum-1024` dataset.
  
  It achieves the following results on the evaluation set:
  - Loss: 2.1846
  - Rouge1: 57.9637
  - Rouge2: 28.7446
  - Rougel: 44.3826
  - Rougelsum: 54.0399
  - Gen Len: 122.77
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  - max input/output is 1024 tokens
  - this is mostly a test because `samsum` is not exactly the best dataset for general-purpose summarization
  
  ## Training and evaluation data
  
  See the dataset card linked on this page for info
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 8
  - eval_batch_size: 4
  - seed: 24915
  - distributed_type: multi-GPU
  - gradient_accumulation_steps: 32
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.02
  - num_epochs: 1.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 0.1195        | 0.17  | 20   | 2.0635          | 57.8829 | 28.7887 | 44.4256 | 54.1299   | 121.8   |
  | 0.1084        | 0.35  | 40   | 2.1178          | 58.0416 | 28.6487 | 44.3905 | 54.1557   | 122.893 |
  | 0.1019        | 0.52  | 60   | 2.1576          | 57.816  | 28.7069 | 44.4242 | 53.9598   | 120.524 |
  | 0.0975        | 0.7   | 80   | 2.1821          | 57.9597 | 28.8178 | 44.4854 | 54.068    | 121.793 |
  | 0.0947        | 0.87  | 100  | 2.1846          | 57.9637 | 28.7446 | 44.3826 | 54.0399   | 122.77  |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu117
  - Datasets 2.6.1
  - Tokenizers 0.13.1