readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  - fnet-bert-base-comparison
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: fnet-base-finetuned-qqp
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QQP
        type: glue
        args: qqp
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8847390551570616
      - name: F1
        type: f1
        value: 0.8466197090382463
  ---
  
  <!-- 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-base-finetuned-qqp
  
  This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QQP dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3686
  - Accuracy: 0.8847
  - F1: 0.8466
  - Combined Score: 0.8657
  
  The model was fine-tuned to compare [google/fnet-base](https://huggingface.co/google/fnet-base) as introduced in [this paper](https://arxiv.org/abs/2105.03824) against [bert-base-cased](https://huggingface.co/bert-base-cased).
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  This model is trained using the [run_glue](https://github.com/huggingface/transformers/blob/master/examples/pytorch/text-classification/run_glue.py) script. The following command was used:
  
  ```bash
  #!/usr/bin/bash
  
  
  #!/usr/bin/bash
  
  
  python ../run_glue.py \\n  --model_name_or_path google/fnet-base \\n  --task_name qqp \\n  --do_train \\n  --do_eval \\n  --max_seq_length 512 \\n  --per_device_train_batch_size 16 \\n  --learning_rate 2e-5 \\n  --num_train_epochs 3 \\n  --output_dir fnet-base-finetuned-qqp \\n  --push_to_hub \\n  --hub_strategy all_checkpoints \\n  --logging_strategy epoch \\n  --save_strategy epoch \\n  --evaluation_strategy epoch \\n```
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.3484        | 1.0   | 22741 | 0.3014          | 0.8676   | 0.8297 | 0.8487         |
  | 0.2387        | 2.0   | 45482 | 0.3011          | 0.8801   | 0.8429 | 0.8615         |
  | 0.1739        | 3.0   | 68223 | 0.3686          | 0.8847   | 0.8466 | 0.8657         |
  
  
  ### Framework versions
  
  - Transformers 4.11.0.dev0
  - Pytorch 1.9.0
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  