readme: ---
  language:
  - en
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_sa_GLUE_Experiment_logit_kd_pretrain_mrpc
    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: 0.5269607843137255
      - name: F1
        type: f1
        value: 0.5089058524173028
  ---
  
  <!-- 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_logit_kd_pretrain_mrpc
  
  This model is a fine-tuned version of [gokuls/distilbert_sa_pre-training-complete](https://huggingface.co/gokuls/distilbert_sa_pre-training-complete) on the GLUE MRPC dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4973
  - Accuracy: 0.5270
  - F1: 0.5089
  - Combined Score: 0.5179
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.5301        | 1.0   | 15   | 0.5200          | 0.3162   | 0.0    | 0.1581         |
  | 0.5164        | 2.0   | 30   | 0.5159          | 0.3162   | 0.0    | 0.1581         |
  | 0.4983        | 3.0   | 45   | 0.5141          | 0.4657   | 0.4076 | 0.4366         |
  | 0.4715        | 4.0   | 60   | 0.5215          | 0.6642   | 0.7187 | 0.6915         |
  | 0.4454        | 5.0   | 75   | 0.4973          | 0.5270   | 0.5089 | 0.5179         |
  | 0.4184        | 6.0   | 90   | 0.4984          | 0.5711   | 0.5803 | 0.5757         |
  | 0.4027        | 7.0   | 105  | 0.5033          | 0.6152   | 0.6424 | 0.6288         |
  | 0.399         | 8.0   | 120  | 0.5095          | 0.7230   | 0.7797 | 0.7514         |
  | 0.3975        | 9.0   | 135  | 0.5087          | 0.6176   | 0.6486 | 0.6331         |
  | 0.3979        | 10.0  | 150  | 0.5088          | 0.7010   | 0.7579 | 0.7295         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  