readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: fnet-large-finetuned-mrpc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MRPC
        type: glue
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8259803921568627
      - name: F1
        type: f1
        value: 0.8798646362098139
  ---
  
  <!-- 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. -->
  
  # fnet-large-finetuned-mrpc
  
  This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large) on the GLUE MRPC dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0872
  - Accuracy: 0.8260
  - F1: 0.8799
  - Combined Score: 0.8529
  
  ## 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: 4
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5656        | 1.0   | 917  | 0.6999          | 0.7843   | 0.8581 | 0.8212         |
  | 0.3874        | 2.0   | 1834 | 0.7280          | 0.8088   | 0.8691 | 0.8390         |
  | 0.1627        | 3.0   | 2751 | 1.1274          | 0.8162   | 0.8780 | 0.8471         |
  | 0.0751        | 4.0   | 3668 | 1.0289          | 0.8333   | 0.8870 | 0.8602         |
  | 0.0339        | 5.0   | 4585 | 1.0872          | 0.8260   | 0.8799 | 0.8529         |
  
  
  ### Framework versions
  
  - Transformers 4.11.0.dev0
  - Pytorch 1.9.0
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  