readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - winograd_wsc
  metrics:
  - rouge
  model-index:
  - name: flan-t5-large-coref
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: winograd_wsc
        type: winograd_wsc
        config: wsc285
        split: test
        args: wsc285
      metrics:
      - name: Rouge1
        type: rouge
        value: 0.9495
  widget:
  - text: "Sam has a Parker pen. He loves writing with it."
    example_title: "Example 1"
  - text: "Coronavirus quickly spread worldwide in 2020. The virus mostly affects elderly people. They can easily catch it."
    example_title: "Example 2"
  - text: "First, the manager evaluates the candidates. Afterwards, he notifies the candidates regarding the evaluation."
    example_title: "Example 3"
  ---
  
  <!-- 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-large-coref
  
  This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the winograd_wsc dataset.  
  
  The model was trained on the task of coreference resolution.  
  
  It achieves the following results on the evaluation set:
  - Loss: 0.2404
  - Rouge1: 0.9495
  - Rouge2: 0.9107
  - Rougel: 0.9494
  - Rougelsum: 0.9494
  - Gen Len: 23.4828
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
  | 1.0169        | 1.0   | 16   | 0.6742          | 0.7918 | 0.6875 | 0.7836 | 0.7847    | 18.2414 |
  | 0.6275        | 2.0   | 32   | 0.5093          | 0.8776 | 0.7947 | 0.8734 | 0.8732    | 21.5517 |
  | 0.596         | 3.0   | 48   | 0.4246          | 0.9104 | 0.8486 | 0.9085 | 0.9091    | 22.5172 |
  | 0.743         | 4.0   | 64   | 0.3632          | 0.9247 | 0.8661 | 0.9235 | 0.9231    | 22.8621 |
  | 0.5007        | 5.0   | 80   | 0.3301          | 0.9353 | 0.8845 | 0.9357 | 0.9353    | 22.8621 |
  | 0.2567        | 6.0   | 96   | 0.3093          | 0.9388 | 0.8962 | 0.9392 | 0.9388    | 22.9655 |
  | 0.4146        | 7.0   | 112  | 0.2978          | 0.9449 | 0.907  | 0.9455 | 0.9458    | 23.1034 |
  | 0.1991        | 8.0   | 128  | 0.2853          | 0.9454 | 0.9064 | 0.946  | 0.9462    | 23.069  |
  | 0.1786        | 9.0   | 144  | 0.2794          | 0.9475 | 0.9097 | 0.9475 | 0.9477    | 23.069  |
  | 0.3559        | 10.0  | 160  | 0.2701          | 0.9424 | 0.9013 | 0.9428 | 0.9426    | 23.0345 |
  | 0.2059        | 11.0  | 176  | 0.2636          | 0.9472 | 0.9069 | 0.9472 | 0.9472    | 23.0345 |
  | 0.199         | 12.0  | 192  | 0.2592          | 0.9523 | 0.9141 | 0.9521 | 0.9524    | 23.4483 |
  | 0.1634        | 13.0  | 208  | 0.2553          | 0.9523 | 0.9141 | 0.9521 | 0.9524    | 23.4483 |
  | 0.2006        | 14.0  | 224  | 0.2518          | 0.9523 | 0.9141 | 0.9521 | 0.9524    | 23.4483 |
  | 0.1419        | 15.0  | 240  | 0.2487          | 0.9523 | 0.9141 | 0.9521 | 0.9524    | 23.4483 |
  | 0.2089        | 16.0  | 256  | 0.2456          | 0.9523 | 0.9141 | 0.9521 | 0.9524    | 23.4483 |
  | 0.1007        | 17.0  | 272  | 0.2431          | 0.9523 | 0.9141 | 0.9521 | 0.9524    | 23.4483 |
  | 0.1598        | 18.0  | 288  | 0.2415          | 0.9495 | 0.9107 | 0.9494 | 0.9494    | 23.4828 |
  | 0.3088        | 19.0  | 304  | 0.2407          | 0.9495 | 0.9107 | 0.9494 | 0.9494    | 23.4828 |
  | 0.2003        | 20.0  | 320  | 0.2404          | 0.9495 | 0.9107 | 0.9494 | 0.9494    | 23.4828 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  