readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - clinc_oos
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-uncased-distilled-clinc
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: clinc_oos
        type: clinc_oos
        args: plus
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9506451612903226
  ---
  
  <!-- 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-distilled-clinc
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2466
  - Accuracy: 0.9506
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 2.9383        | 1.0   | 954  | 1.4511          | 0.8397   |
  | 0.8485        | 2.0   | 1908 | 0.4733          | 0.9255   |
  | 0.2822        | 3.0   | 2862 | 0.3070          | 0.9429   |
  | 0.1515        | 4.0   | 3816 | 0.2664          | 0.9490   |
  | 0.106         | 5.0   | 4770 | 0.2641          | 0.95     |
  | 0.0874        | 6.0   | 5724 | 0.2536          | 0.9510   |
  | 0.0764        | 7.0   | 6678 | 0.2475          | 0.9506   |
  | 0.0718        | 8.0   | 7632 | 0.2450          | 0.9513   |
  | 0.068         | 9.0   | 8586 | 0.2473          | 0.9497   |
  | 0.0664        | 10.0  | 9540 | 0.2466          | 0.9506   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0
  - Datasets 1.16.1
  - Tokenizers 0.12.1
  