readme: ---
  tags:
  - generated_from_trainer
  - summarization
  - book summary
  metrics:
  - rouge
  dataset:
  - kmfoda/booksum
  model-index:
  - name: long-t5-tglobal-large-booksum-WIP
    results:
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: kmfoda/booksum
        type: kmfoda/booksum
        config: kmfoda--booksum
        split: test
      metrics:
      - type: rouge
        value: 25.6136
        name: ROUGE-1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiY2E3ZWI5NjRiZGE3YTQ2YTg5MGNmNzI5NTdjN2U3OTNiNzhmMjBhMDVkZjcwZjg0MTEyMTM3MzQyZmI1NzNjYSIsInZlcnNpb24iOjF9.REYAFwePFucxAn1Twsh9BSov9KPsCML9nTjL9oIIWa3Hp8DwJ_syPmfNsYxGe2vvNVq5rzBKF9gsJW80pbo-Aw
      - type: rouge
        value: 2.8652
        name: ROUGE-2
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzk5Mjg0ZmRjYzg1NjM4MGMwOWYyOTM0ZDU2OTM2ZGJlYmM0OTVjNTI2NzcyMzU0MGI0M2I0ZmE0ZmY2NmRlNSIsInZlcnNpb24iOjF9.MzKSIqRjIV6V5YMYlvbRt2ca_CR5WFZ8DqOrUvDbiSyh7qbdU6F2LdDjB6eL-wzIR_DMF10sTtoF7H7wXs2GDw
      - type: rouge
        value: 12.4913
        name: ROUGE-L
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDMzMDZhYzg2N2Q0YTZiYWUzOGI2MTRjMmRlNGIzY2I0ZDU3YzQ1MWVkZDlkOTQzNDlhNjk1MWM2OWUwNDczYSIsInZlcnNpb24iOjF9.TysgYlvfe-4GJWDSFg8KQ97Bsu-kDX3VDamS6bi9q_60V3mBzIOz0M0slySuHXu5S4MJ8a0OCPWvskP0T4ZmCQ
      - type: rouge
        value: 23.1102
        name: ROUGE-LSUM
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzY3NmI2MDJkZTQ2MzMwMDg2NWZmM2Q5NjNmZTRkMTJiODViODZmODYyNTgwMzBkYzBmZDRmMWNjYjg5NjBkYSIsInZlcnNpb24iOjF9.XNvINLow-1mfiDbm_YcAM_l4c-gEV_V5oLKzBWh7Hdmi9gHP_Z86fqQn9Kj2nhOPFWcUOFUBIzx4Z0rjs162BA
      - type: loss
        value: 5.004334926605225
        name: loss
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODNjMzI5N2IwNDExOWQzMWYxMzE4YzkxYWYxZmRkNTA2NWQ1MmYzOTFjODJhNGUzODQxYmNkODBlZDA0MGNmZCIsInZlcnNpb24iOjF9.xGNlloXeHra0K5DTKXbsrrkyuAvFXZwjzkxOyjtpw2jWs0KPw4nQ1MKkJiX6juXtleJrvS2u1FQcwCbygUmLDQ
      - type: gen_len
        value: 89.4354
        name: gen_len
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjBlODBiMmEwN2UzYzE5NTE3ODBkNDVmMTgxMzhlYmVmZjgxMzJjYTBlYjBhMDgzNzhhMWQ0Mzc2MjdjN2E0ZiIsInZlcnNpb24iOjF9.Z9kytQDiNK-TCaHz-0YZeH8FCrW5D0SA-ji7Q86wqdhBC9jTDmJGnBll6mGFcHipERrRKZb12hYStKJanb3iBA
  ---
  
  
  # tglobal-large-booksum-WIP
  
  > this is a WIP checkpoint that has been fine-tuned from the vanilla (original) for 10ish epochs. It is **not ready to be used for inference**
  
  This model is a fine-tuned version of [google/long-t5-tglobal-large](https://huggingface.co/google/long-t5-tglobal-large) on the `kmfoda/booksum` dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.9519
  - Rouge1: 21.8058
  - Rouge2: 2.9343
  - Rougel: 10.3717
  - Rougelsum: 20.1537
  - Gen Len: 106.055
  
  ## Model description
  
  Testing fine-tuning only on booksum with 16384/1024 the whole time (vs. previous large WIP checkpoint I made that started from a partially-trained `pubmed` checkpoint)
  
  ## Intended uses & limitations
  
  this is a WIP checkpoint that has been fine-tuned from the vanilla (original) for 10ish epochs. It is **not ready to be used for inference**
  
  ## Training and evaluation data
  
  This is **only** fine-tuned on booksum (vs. previous large WIP checkpoint I made that started from a partially-trained `pubmed` checkpoint)
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0004
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 31060
  - distributed_type: multi-GPU
  - num_devices: 4
  - gradient_accumulation_steps: 32
  - total_train_batch_size: 128
  - total_eval_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Gen Len | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
  |:-------------:|:-----:|:----:|:-------:|:---------------:|:-------:|:------:|:-------:|:---------:|
  | 5.0389        | 0.99  | 37   | 219.03  | 5.1884          | 29.995  | 4.4045 | 12.8837 | 27.557    |
  | 4.8986        | 1.0   | 75   | 5.1286  | 26.921          | 3.7193  | 11.3605| 25.3492 | 276.005   |
  | 4.5928        | 2.0   | 150  | 4.9900  | 26.6667         | 3.7342  | 11.8223| 24.7087 | 178.775   |
  | 4.6159        | 3.0   | 225  | 4.9519  | 21.8058         | 2.9343  | 10.3717| 20.1537 | 106.055   |
  
  
  #### eval in bf16
  
  
  ```
  ***** eval metrics *****
    epoch                   =        3.0
    eval_gen_len            =    103.075
    eval_loss               =     4.9501
    eval_rouge1             =    21.6345
    eval_rouge2             =      2.877
    eval_rougeL             =     10.386
    eval_rougeLsum          =    20.0148
    eval_runtime            = 0:06:02.75
    eval_samples            =        200
    eval_samples_per_second =      0.551
    eval_steps_per_second   =      0.1