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: 0.9393548387096774
  ---
  
  <!-- 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 with knowledge distillation version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the [NLP with Transformers book](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/). You can find the full code in the accompanying [Github repository](https://github.com/nlp-with-transformers/notebooks/blob/main/08_model-compression.ipynb).
  
  It achieves the following results on the evaluation set:
  - Loss: 0.1005
  - Accuracy: 0.9394
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.9031        | 1.0   | 318  | 0.5745          | 0.7365   |
  | 0.4481        | 2.0   | 636  | 0.2856          | 0.8748   |
  | 0.2528        | 3.0   | 954  | 0.1798          | 0.9187   |
  | 0.176         | 4.0   | 1272 | 0.1398          | 0.9294   |
  | 0.1416        | 5.0   | 1590 | 0.1211          | 0.9348   |
  | 0.1243        | 6.0   | 1908 | 0.1116          | 0.9348   |
  | 0.1133        | 7.0   | 2226 | 0.1062          | 0.9377   |
  | 0.1075        | 8.0   | 2544 | 0.1035          | 0.9387   |
  | 0.1039        | 9.0   | 2862 | 0.1014          | 0.9381   |
  | 0.1018        | 10.0  | 3180 | 0.1005          | 0.9394   |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.1+cu102
  - Datasets 1.13.0
  - Tokenizers 0.10.3
  