readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - winograd_wsc
  metrics:
  - rouge
  model-index:
  - name: flan-t5-small-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: 0.906
  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-small-coref
  
  This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) 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: 0.5656
  - Rouge1: 0.906
  - Rouge2: 0.8192
  - Rougel: 0.9016
  - Rougelsum: 0.9026
  - Gen Len: 23.1724
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
  | No log        | 1.0   | 16   | 1.0901          | 0.6849 | 0.561  | 0.6734 | 0.6746    | 18.4483 |
  | No log        | 2.0   | 32   | 0.9083          | 0.8512 | 0.7509 | 0.8438 | 0.8437    | 21.1379 |
  | No log        | 3.0   | 48   | 0.8132          | 0.8638 | 0.7728 | 0.8588 | 0.8595    | 21.8276 |
  | No log        | 4.0   | 64   | 0.7590          | 0.8786 | 0.7842 | 0.8744 | 0.876     | 22.2069 |
  | No log        | 5.0   | 80   | 0.7225          | 0.8846 | 0.7928 | 0.8805 | 0.8817    | 22.3793 |
  | No log        | 6.0   | 96   | 0.6920          | 0.886  | 0.7942 | 0.8821 | 0.8827    | 22.4483 |
  | No log        | 7.0   | 112  | 0.6660          | 0.8861 | 0.7922 | 0.8816 | 0.8827    | 22.5172 |
  | No log        | 8.0   | 128  | 0.6470          | 0.8879 | 0.7953 | 0.8836 | 0.8849    | 22.6897 |
  | No log        | 9.0   | 144  | 0.6318          | 0.8968 | 0.806  | 0.8923 | 0.8933    | 23.069  |
  | No log        | 10.0  | 160  | 0.6160          | 0.8968 | 0.806  | 0.8923 | 0.8933    | 23.069  |
  | No log        | 11.0  | 176  | 0.6055          | 0.9056 | 0.822  | 0.9014 | 0.9021    | 23.1724 |
  | No log        | 12.0  | 192  | 0.5962          | 0.9056 | 0.822  | 0.9014 | 0.9021    | 23.1724 |
  | No log        | 13.0  | 208  | 0.5884          | 0.9074 | 0.8246 | 0.9033 | 0.9042    | 23.2069 |
  | No log        | 14.0  | 224  | 0.5825          | 0.9049 | 0.8182 | 0.9005 | 0.9016    | 23.2414 |
  | No log        | 15.0  | 240  | 0.5769          | 0.9049 | 0.8182 | 0.9005 | 0.9016    | 23.2414 |
  | No log        | 16.0  | 256  | 0.5727          | 0.903  | 0.8132 | 0.8991 | 0.8997    | 23.1724 |
  | No log        | 17.0  | 272  | 0.5698          | 0.906  | 0.8192 | 0.9016 | 0.9026    | 23.1724 |
  | No log        | 18.0  | 288  | 0.5673          | 0.906  | 0.8192 | 0.9016 | 0.9026    | 23.1724 |
  | No log        | 19.0  | 304  | 0.5661          | 0.906  | 0.8192 | 0.9016 | 0.9026    | 23.1724 |
  | No log        | 20.0  | 320  | 0.5656          | 0.906  | 0.8192 | 0.9016 | 0.9026    | 23.1724 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  