readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  - fnet-bert-base-comparison
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: fnet-base-finetuned-qnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QNLI
        type: glue
        args: qnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8438586857038257
  ---
  
  <!-- 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-qnli
  
  This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base) on the GLUE QNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4746
  - Accuracy: 0.8439
  
  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
  
  python ../run_glue.py \\n  --model_name_or_path google/fnet-base \\n  --task_name qnli \\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-qnli \\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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.4597        | 1.0   | 6547  | 0.3713          | 0.8411   |
  | 0.3252        | 2.0   | 13094 | 0.3781          | 0.8420   |
  | 0.2243        | 3.0   | 19641 | 0.4746          | 0.8439   |
  
  
  ### Framework versions
  
  - Transformers 4.11.0.dev0
  - Pytorch 1.9.0
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  