readme: ---
  language:
  - en
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: roberta-base-qnli
    results:
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: GLUE QNLI
        type: glue
        args: qnli
      metrics:
      - type: accuracy
        value: 0.9245835621453414
        name: Accuracy
    - task:
        type: natural-language-inference
        name: Natural Language Inference
      dataset:
        name: glue
        type: glue
        config: qnli
        split: validation
      metrics:
      - type: accuracy
        value: 0.924400512538898
        name: Accuracy
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmE1ZDY2YTAzNDFiNDdlMGFlNjk2OTkyNjVlMjgwNDJjMzBlMzkwMGZjOWNhZmY2OWFiZjVmOGZlZmU5OGUxNCIsInZlcnNpb24iOjF9._WT9aiP0YGqyVIBSqUt5E6MT6EjB8g2ol_xbl0d1RGLev-eYtACpvAex_qckbXcxqFSENjVqtGx24MqXvQZyAA
      - type: precision
        value: 0.9171997157071784
        name: Precision
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDg3ZGEwNTNmZjc2ZDNmZGY5NzgzMDRlMzBiODc0ZDY2NDE5NDRiYzNmYzg4YzQ5ZGM0MmI0ODA5NjQ3OTcxMiIsInZlcnNpb24iOjF9.CCCWPcZ3Ut8yjdal-62KxakOqVF7Vfj_A6etOxRV4pUa1WSpdOtK4BobR59tJKtfUw_l-h32EMMGQK0ZQBNCAA
      - type: recall
        value: 0.9348062296269467
        name: Recall
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDI0OTNkOWQ2NGYzYTQ5ZDcwNjk1NDJhYTMzNWQ2ZTkyZDcxZTA5OTFkZTNjZDBmMGZjMDQ4YmI2M2Y3ZWE2YSIsInZlcnNpb24iOjF9.gfgQq9FgLkOA4cBylEAVoJZLupqglQusjnpyd3MAk1zxLeFhYSQOiRmjjW2nPNV2cJM43bR4XPsqePWzWimzDA
      - type: auc
        value: 0.9744865501321541
        name: AUC
        verified: true
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      - type: f1
        value: 0.9259192825112107
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWVjN2E1YWNkMDgyMTk0Yjc2ZGFhYzJjNjFkY2VmNmU0NjNjZWQ3N2ZhYzgzNTg2N2FlNmY4YmMyYzJkNjFhOSIsInZlcnNpb24iOjF9.I1dkHU12MMeZerjCJ8JfBMyaR1fCEHvTZfpZN-hD2hTITjgkFcTFC_jFvydSwzKo7yX0ztA5ID3qqgW4qD7bAQ
      - type: loss
        value: 0.2990749478340149
        name: loss
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTM2ZjAwOWNjNWE3NjcwYTVmZTIyY2YzNGI3Mzk5ZjM0YjVmYjg3ODA4Mjc3NWViMDkxMDlmZWRiNTdiOGNjMCIsInZlcnNpb24iOjF9.ODKlAkIeFLR4XiugSVARPvDgVUf6bQas9gSm8r_Q8xzZISaVIOUKNs2Z7kq443LiBBulvBoPaapNPpwkBbMkAw
  ---
  
  <!-- 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-qnli
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE QNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2992
  - Accuracy: 0.9246
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.2986        | 1.0   | 6547  | 0.2215          | 0.9171   |
  | 0.243         | 2.0   | 13094 | 0.2321          | 0.9173   |
  | 0.2048        | 3.0   | 19641 | 0.2992          | 0.9246   |
  | 0.1629        | 4.0   | 26188 | 0.3538          | 0.9220   |
  | 0.1308        | 5.0   | 32735 | 0.3533          | 0.9209   |
  | 0.0846        | 6.0   | 39282 | 0.4277          | 0.9229   |
  
  
  ### Framework versions
  
  - Transformers 4.20.0.dev0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  