readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_sa_GLUE_Experiment_mrpc_96
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MRPC
        type: glue
        config: mrpc
        split: validation
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6887254901960784
      - name: F1
        type: f1
        value: 0.7829059829059829
  ---
  
  <!-- 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. -->
  
  # distilbert_sa_GLUE_Experiment_mrpc_96
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MRPC dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5873
  - Accuracy: 0.6887
  - F1: 0.7829
  - Combined Score: 0.7358
  
  ## 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: 5e-05
  - train_batch_size: 256
  - eval_batch_size: 256
  - seed: 10
  - distributed_type: multi-GPU
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.6677        | 1.0   | 15   | 0.6479          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6455        | 2.0   | 30   | 0.6395          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6399        | 3.0   | 45   | 0.6331          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6361        | 4.0   | 60   | 0.6288          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6352        | 5.0   | 75   | 0.6262          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6315        | 6.0   | 90   | 0.6252          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6331        | 7.0   | 105  | 0.6244          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6292        | 8.0   | 120  | 0.6242          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6314        | 9.0   | 135  | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6296        | 10.0  | 150  | 0.6242          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6306        | 11.0  | 165  | 0.6241          | 0.6838   | 0.8122 | 0.7480         |
  | 0.63          | 12.0  | 180  | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6337        | 13.0  | 195  | 0.6240          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6299        | 14.0  | 210  | 0.6239          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6297        | 15.0  | 225  | 0.6230          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6248        | 16.0  | 240  | 0.6187          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6065        | 17.0  | 255  | 0.5999          | 0.6936   | 0.8164 | 0.7550         |
  | 0.5624        | 18.0  | 270  | 0.6007          | 0.6838   | 0.7659 | 0.7249         |
  | 0.5185        | 19.0  | 285  | 0.5891          | 0.6838   | 0.7772 | 0.7305         |
  | 0.4664        | 20.0  | 300  | 0.5873          | 0.6887   | 0.7829 | 0.7358         |
  | 0.4248        | 21.0  | 315  | 0.5893          | 0.6936   | 0.7764 | 0.7350         |
  | 0.3844        | 22.0  | 330  | 0.5949          | 0.7010   | 0.7798 | 0.7404         |
  | 0.3551        | 23.0  | 345  | 0.5942          | 0.7034   | 0.7866 | 0.7450         |
  | 0.3314        | 24.0  | 360  | 0.6040          | 0.7034   | 0.7881 | 0.7458         |
  | 0.3181        | 25.0  | 375  | 0.6162          | 0.7010   | 0.7867 | 0.7438         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  