readme: ---
  language:
  - en
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: roberta-base-qqp
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QQP
        type: glue
        args: qqp
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9013109077417759
      - name: F1
        type: f1
        value: 0.866644385026738
  ---
  
  <!-- 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. -->
  
  # roberta-base-qqp
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE QQP dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2445
  - Accuracy: 0.9013
  - F1: 0.8666
  - Combined Score: 0.8840
  
  ## 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: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.06
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:|
  | 0.6894        | 0.02  | 500    | 0.6476          | 0.6318   | 0.0    | 0.3159         |
  | 0.5601        | 0.04  | 1000   | 0.4628          | 0.7744   | 0.7412 | 0.7578         |
  | 0.4373        | 0.07  | 1500   | 0.3894          | 0.8201   | 0.7759 | 0.7980         |
  | 0.3995        | 0.09  | 2000   | 0.3765          | 0.8320   | 0.7761 | 0.8041         |
  | 0.3912        | 0.11  | 2500   | 0.3725          | 0.8352   | 0.7939 | 0.8145         |
  | 0.3769        | 0.13  | 3000   | 0.3423          | 0.8451   | 0.8010 | 0.8231         |
  | 0.3747        | 0.15  | 3500   | 0.3513          | 0.8377   | 0.8028 | 0.8202         |
  | 0.3529        | 0.18  | 4000   | 0.3271          | 0.8560   | 0.8144 | 0.8352         |
  | 0.3666        | 0.2   | 4500   | 0.3174          | 0.8565   | 0.8066 | 0.8316         |
  | 0.3455        | 0.22  | 5000   | 0.3128          | 0.8604   | 0.8148 | 0.8376         |
  | 0.3373        | 0.24  | 5500   | 0.3088          | 0.8665   | 0.8230 | 0.8448         |
  | 0.3405        | 0.26  | 6000   | 0.3130          | 0.8593   | 0.8235 | 0.8414         |
  | 0.338         | 0.29  | 6500   | 0.3135          | 0.8646   | 0.8251 | 0.8449         |
  | 0.3351        | 0.31  | 7000   | 0.3019          | 0.8682   | 0.8307 | 0.8495         |
  | 0.3252        | 0.33  | 7500   | 0.3042          | 0.8678   | 0.8332 | 0.8505         |
  | 0.3343        | 0.35  | 8000   | 0.3123          | 0.8648   | 0.8087 | 0.8367         |
  | 0.3365        | 0.37  | 8500   | 0.3369          | 0.8690   | 0.8286 | 0.8488         |
  | 0.3344        | 0.4   | 9000   | 0.2991          | 0.8705   | 0.8253 | 0.8479         |
  | 0.33          | 0.42  | 9500   | 0.3326          | 0.8682   | 0.8370 | 0.8526         |
  | 0.3202        | 0.44  | 10000  | 0.2986          | 0.8743   | 0.8360 | 0.8552         |
  | 0.3255        | 0.46  | 10500  | 0.2873          | 0.8754   | 0.8351 | 0.8552         |
  | 0.3098        | 0.48  | 11000  | 0.2956          | 0.8741   | 0.8255 | 0.8498         |
  | 0.3157        | 0.51  | 11500  | 0.2895          | 0.8729   | 0.8372 | 0.8550         |
  | 0.3098        | 0.53  | 12000  | 0.2874          | 0.8741   | 0.8253 | 0.8497         |
  | 0.3015        | 0.55  | 12500  | 0.3153          | 0.8658   | 0.8156 | 0.8407         |
  | 0.3106        | 0.57  | 13000  | 0.2925          | 0.8737   | 0.8258 | 0.8497         |
  | 0.3052        | 0.59  | 13500  | 0.3320          | 0.8778   | 0.8369 | 0.8573         |
  | 0.31          | 0.62  | 14000  | 0.3108          | 0.8714   | 0.8163 | 0.8438         |
  | 0.3022        | 0.64  | 14500  | 0.2910          | 0.8802   | 0.8398 | 0.8600         |
  | 0.302         | 0.66  | 15000  | 0.2871          | 0.8775   | 0.8294 | 0.8535         |
  | 0.3107        | 0.68  | 15500  | 0.2933          | 0.8809   | 0.8475 | 0.8642         |
  | 0.3206        | 0.7   | 16000  | 0.2718          | 0.8836   | 0.8461 | 0.8648         |
  | 0.2941        | 0.73  | 16500  | 0.2920          | 0.8775   | 0.8371 | 0.8573         |
  | 0.3097        | 0.75  | 17000  | 0.2720          | 0.8853   | 0.8457 | 0.8655         |
  | 0.2946        | 0.77  | 17500  | 0.3131          | 0.8823   | 0.8428 | 0.8626         |
  | 0.2954        | 0.79  | 18000  | 0.2824          | 0.8844   | 0.8457 | 0.8651         |
  | 0.2874        | 0.81  | 18500  | 0.2771          | 0.8829   | 0.8360 | 0.8595         |
  | 0.2903        | 0.84  | 19000  | 0.2947          | 0.8863   | 0.8452 | 0.8