readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-mnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        config: mnli
        split: train
        args: mnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6812022414671421
  ---
  
  <!-- 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-base-uncased-finetuned-mnli
  
  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: 2.4896
  - Accuracy: 0.6812
  
  ## 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: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.3128        | 1.0   | 625  | 1.1219          | 0.6772   |
  | 0.1801        | 2.0   | 1250 | 1.4366          | 0.6716   |
  | 0.1304        | 3.0   | 1875 | 1.8460          | 0.6777   |
  | 0.1377        | 4.0   | 2500 | 1.9691          | 0.6702   |
  | 0.0949        | 5.0   | 3125 | 2.1351          | 0.6792   |
  | 0.0693        | 6.0   | 3750 | 2.3718          | 0.6797   |
  | 0.0581        | 7.0   | 4375 | 2.4629          | 0.6801   |
  | 0.0333        | 8.0   | 5000 | 2.4896          | 0.6812   |
  | 0.0239        | 9.0   | 5625 | 2.5793          | 0.6783   |
  | 0.0148        | 10.0  | 6250 | 2.5962          | 0.6791   |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  