readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: bert-base-cased-qqp
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QQP
        type: glue
        args: qqp
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8980707395498392
      - name: F1
        type: f1
        value: 0.8616696317679836
  ---
  
  <!-- 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. -->
  
  # bert-base-cased-qqp
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE QQP dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2437
  - Accuracy: 0.8981
  - F1: 0.8617
  - Combined Score: 0.8799
  
  ## 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 |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:--------------:|
  | 0.7007        | 0.02  | 500    | 0.6597          | 0.6318   | 0.0    | 0.3159         |
  | 0.5828        | 0.04  | 1000   | 0.5143          | 0.7203   | 0.6773 | 0.6988         |
  | 0.4982        | 0.07  | 1500   | 0.4520          | 0.7737   | 0.7212 | 0.7474         |
  | 0.4561        | 0.09  | 2000   | 0.4235          | 0.7947   | 0.7503 | 0.7725         |
  | 0.4404        | 0.11  | 2500   | 0.4087          | 0.8047   | 0.7656 | 0.7851         |
  | 0.4167        | 0.13  | 3000   | 0.3833          | 0.8203   | 0.7744 | 0.7974         |
  | 0.4068        | 0.15  | 3500   | 0.3952          | 0.8120   | 0.7781 | 0.7951         |
  | 0.3925        | 0.18  | 4000   | 0.3661          | 0.8298   | 0.7918 | 0.8108         |
  | 0.386         | 0.2   | 4500   | 0.3543          | 0.8371   | 0.7983 | 0.8177         |
  | 0.368         | 0.22  | 5000   | 0.3411          | 0.8457   | 0.7932 | 0.8194         |
  | 0.3614        | 0.24  | 5500   | 0.3342          | 0.8506   | 0.8047 | 0.8276         |
  | 0.3618        | 0.26  | 6000   | 0.3287          | 0.8527   | 0.8076 | 0.8301         |
  | 0.3504        | 0.29  | 6500   | 0.3386          | 0.8481   | 0.8081 | 0.8281         |
  | 0.3562        | 0.31  | 7000   | 0.3232          | 0.8576   | 0.8158 | 0.8367         |
  | 0.3417        | 0.33  | 7500   | 0.3364          | 0.8589   | 0.8097 | 0.8343         |
  | 0.342         | 0.35  | 8000   | 0.3208          | 0.8618   | 0.8100 | 0.8359         |
  | 0.3399        | 0.37  | 8500   | 0.3242          | 0.8587   | 0.8185 | 0.8386         |
  | 0.345         | 0.4   | 9000   | 0.3111          | 0.8631   | 0.8159 | 0.8395         |
  | 0.3258        | 0.42  | 9500   | 0.3327          | 0.8588   | 0.8246 | 0.8417         |
  | 0.3305        | 0.44  | 10000  | 0.3285          | 0.8623   | 0.8174 | 0.8399         |
  | 0.3351        | 0.46  | 10500  | 0.3157          | 0.8667   | 0.8186 | 0.8426         |
  | 0.3132        | 0.48  | 11000  | 0.2976          | 0.8680   | 0.8214 | 0.8447         |
  | 0.3214        | 0.51  | 11500  | 0.3048          | 0.8672   | 0.8236 | 0.8454         |
  | 0.3175        | 0.53  | 12000  | 0.3184          | 0.8710   | 0.8227 | 0.8468         |
  | 0.316         | 0.55  | 12500  | 0.3084          | 0.8647   | 0.8299 | 0.8473         |
  | 0.3178        | 0.57  | 13000  | 0.3053          | 0.8663   | 0.8152 | 0.8407         |
  | 0.3009        | 0.59  | 13500  | 0.3168          | 0.8710   | 0.8321 | 0.8515         |
  | 0.3091        | 0.62  | 14000  | 0.3101          | 0.8683   | 0.8224 | 0.8454         |
  | 0.3102        | 0.64  | 14500  | 0.2936          | 0.8720   | 0.8306 | 0.8513         |
  | 0.3007        | 0.66  | 15000  | 0.2953          | 0.8752   | 0.8371 | 0.8561         |
  | 0.3123        | 0.68  | 15500  | 0.2992          | 0.8770   | 0.8389 | 0.8580         |
  | 0.3212        | 0.7   | 16000  | 0.2852          | 0.8772   | 0.8335 | 0.8553         |
  | 0.2954        | 0.73  | 16500  | 0.2874          | 0.8736   | 0.8288 | 0.8512         |
  | 0.3097        | 0.75  | 17000  | 0.2833          | 0.8791   | 0.8358 | 0.8574         |
  | 0.3013        | 0.77  | 17500  | 0.2893          | 0.8776   | 0.8333 | 0.8555         |
  | 0.2982        | 0.79  | 18000  | 0.2843          | 0.8805   | 0.8383 | 0.8594         |
  | 0.3019        | 0.81  | 18500  | 0.2817          | 0.8787   | 0.8319 | 0.8553         |
  | 0.2922        | 0.84  | 19000  | 0.3019          | 0.8