readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: Glue_distilbert_new
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        config: mrpc
        split: validation
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6397058823529411
      - name: F1
        type: f1
        value: 0.7360861759425494
  ---
  
  <!-- 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. -->
  
  # Glue_distilbert_new
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6153
  - Accuracy: 0.6397
  - F1: 0.7361
  - Combined Score: 0.6879
  
  ## 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: 1024
  - eval_batch_size: 1024
  - seed: 33
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.6415        | 1.0   | 4    | 0.6363          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6292        | 2.0   | 8    | 0.6101          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6244        | 3.0   | 12   | 0.6047          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6075        | 4.0   | 16   | 0.6153          | 0.6397   | 0.7361 | 0.6879         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.11.6
  