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: 0.9467741935483871
  ---
  
  <!-- 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: 0.2795
  - Accuracy: 0.9468
  
  ## 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: 48
  - eval_batch_size: 48
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 3.4223        | 1.0   | 318  | 2.5556          | 0.7561   |
  | 1.9655        | 2.0   | 636  | 1.3075          | 0.8577   |
  | 1.0041        | 3.0   | 954  | 0.6970          | 0.9165   |
  | 0.5449        | 4.0   | 1272 | 0.4637          | 0.9339   |
  | 0.3424        | 5.0   | 1590 | 0.3630          | 0.9397   |
  | 0.247         | 6.0   | 1908 | 0.3225          | 0.9442   |
  | 0.1968        | 7.0   | 2226 | 0.2983          | 0.9458   |
  | 0.1693        | 8.0   | 2544 | 0.2866          | 0.9465   |
  | 0.1547        | 9.0   | 2862 | 0.2820          | 0.9468   |
  | 0.1477        | 10.0  | 3180 | 0.2795          | 0.9468   |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.10.0+cu111
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  